Datasets:

ftopal

/

huggingface-models

like 0

transformers

# distilrubert-tiny-cased-conversational Conversational DistilRuBERT-tiny \(Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)). It can be considered as tiny copy of [Conversational DistilRuBERT-small](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational). Our DistilRuBERT-tiny is highly inspired by \[3\], \[4\] and architecture is very close to \[5\]. Namely, we use * MLM loss (between token labels and student output distribution) * MSE loss (between averaged student and teacher hidden states) The key features are: * unlike most of distilled language models, we **didn't** use KL loss during pre-training * reduced vocabulary size (30K in *tiny* vs. 100K in *base* and *small* ) * two separate inputs for student: tokens obtained using student tokenizer (for MLM) and teacher tokens greedily splitted by student tokens (for MSE) Here is comparison between teacher model (`Conversational RuBERT`) and other distilled models. | Model name | \# params, M | \# vocab, K | Mem., MB | |---|---|---|---| | `rubert-base-cased-conversational` | 177.9 | 120 | 679 | | `distilrubert-base-cased-conversational` | 135.5 | 120 | 517 | | `distilrubert-small-cased-conversational` | 107.1 | 120 | 409 | | `cointegrated/rubert-tiny` | 11.8 | **30** | 46 | | **distilrubert-tiny-cased-conversational** | **10.4** | 31 | **41** | DistilRuBERT-tiny was trained for about 100 hrs. on 7 nVIDIA Tesla P100-SXM2.0 16Gb. We used `PyTorchBenchmark` from `transformers` to evaluate model's performance and compare it with other pre-trained language models for Russian. All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb. | Model name | Batch size | Seq len | Time, s || Mem, MB || |---|---|---|------||------|| | | | | CPU | GPU | CPU | GPU | | `rubert-base-cased-conversational` | 1 | 512 | 0.147 | 0.014 | 897 | 1531 | | `distilrubert-base-cased-conversational` | 1 | 512 | 0.083 | 0.006 | 766 | 1423 | | `distilrubert-small-cased-conversational` | 1 | 512 | 0.03 | **0.002** | 600 | 1243 | | `cointegrated/rubert-tiny` | 1 | 512 | 0.041 | 0.003 | 272 | 919 | | **distilrubert-tiny-cased-conversational** | 1 | 512 | **0.023** | 0.003 | **206** | **855** | | `rubert-base-cased-conversational` | 16 | 512 | 2.839 | 0.182 | 1499 | 2071 | | `distilrubert-base-cased-conversational` | 16 | 512 | 1.065 | 0.055 | 2541 | 2927 | | `distilrubert-small-cased-conversational` | 16 | 512 | 0.373 | **0.003** | 1360 | 1943 | | `cointegrated/rubert-tiny` | 16 | 512 | 0.628 | 0.004 | 1293 | 2221 | | **distilrubert-tiny-cased-conversational** | 16 | 512 | **0.219** | **0.003** | **633** | **1291** | To evaluate model quality, we fine-tuned DistilRuBERT-tiny on classification (RuSentiment, ParaPhraser), NER and question answering data sets for Russian and obtained scores very similar to the [Conversational DistilRuBERT-small](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational). # Citation If you found the model useful for your research, we are kindly ask to cite [this](https://arxiv.org/abs/2205.02340) paper: ``` @misc{https://doi.org/10.48550/arxiv.2205.02340, doi = {10.48550/ARXIV.2205.02340}, url = {https://arxiv.org/abs/2205.02340}, author = {Kolesnikova, Alina and Kuratov, Yuri and Konovalov, Vasily and Burtsev, Mikhail}, keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Knowledge Distillation of Russian Language Models with Reduction of Vocabulary}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` \[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\) \[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017. \[3\]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. \(2019\). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. \[4\]: <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation> \[5\]: <https://habr.com/ru/post/562064/>, <https://huggingface.co/cointegrated/rubert-tiny>

{"language": ["ru"]}

DeepPavlov/distilrubert-tiny-cased-conversational-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #ru #arxiv-2205.02340 #endpoints_compatible #region-us

distilrubert-tiny-cased-conversational ====================================== Conversational DistilRuBERT-tiny (Russian, cased, 3‑layers, 264‑hidden, 12‑heads, 10.4M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT). It can be considered as tiny copy of Conversational DistilRuBERT-small. Our DistilRuBERT-tiny is highly inspired by [3], [4] and architecture is very close to [5]. Namely, we use * MLM loss (between token labels and student output distribution) * MSE loss (between averaged student and teacher hidden states) The key features are: * unlike most of distilled language models, we didn't use KL loss during pre-training * reduced vocabulary size (30K in *tiny* vs. 100K in *base* and *small* ) * two separate inputs for student: tokens obtained using student tokenizer (for MLM) and teacher tokens greedily splitted by student tokens (for MSE) Here is comparison between teacher model ('Conversational RuBERT') and other distilled models. DistilRuBERT-tiny was trained for about 100 hrs. on 7 nVIDIA Tesla P100-SXM2.0 16Gb. We used 'PyTorchBenchmark' from 'transformers' to evaluate model's performance and compare it with other pre-trained language models for Russian. All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb. | Model name | Batch size | Seq len | Time, s || Mem, MB || |---|---|---|------||------|| | | | | CPU | GPU | CPU | GPU | | 'rubert-base-cased-conversational' | 1 | 512 | 0.147 | 0.014 | 897 | 1531 | | 'distilrubert-base-cased-conversational' | 1 | 512 | 0.083 | 0.006 | 766 | 1423 | | 'distilrubert-small-cased-conversational' | 1 | 512 | 0.03 | 0.002 | 600 | 1243 | | 'cointegrated/rubert-tiny' | 1 | 512 | 0.041 | 0.003 | 272 | 919 | | distilrubert-tiny-cased-conversational | 1 | 512 | 0.023 | 0.003 | 206 | 855 | | 'rubert-base-cased-conversational' | 16 | 512 | 2.839 | 0.182 | 1499 | 2071 | | 'distilrubert-base-cased-conversational' | 16 | 512 | 1.065 | 0.055 | 2541 | 2927 | | 'distilrubert-small-cased-conversational' | 16 | 512 | 0.373 | 0.003 | 1360 | 1943 | | 'cointegrated/rubert-tiny' | 16 | 512 | 0.628 | 0.004 | 1293 | 2221 | | distilrubert-tiny-cased-conversational | 16 | 512 | 0.219 | 0.003 | 633 | 1291 | To evaluate model quality, we fine-tuned DistilRuBERT-tiny on classification (RuSentiment, ParaPhraser), NER and question answering data sets for Russian and obtained scores very similar to the Conversational DistilRuBERT-small. If you found the model useful for your research, we are kindly ask to cite this paper: [1]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) [2]: Shavrina T., Shapovalova O. (2017) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017. [3]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. [4]: <URL [5]: <URL <URL

transformers

WARNING: This is `distilrubert-small-cased-conversational` model uploaded with wrong name. This one is the same as [distilrubert-small-cased-conversational](https://huggingface.co/DeepPavlov/distilrubert-small-cased-conversational). `distilrubert-tiny-cased-conversational` could be found in [distilrubert-tiny-cased-conversational-v1](https://huggingface.co/DeepPavlov/distilrubert-tiny-cased-conversational-v1). # distilrubert-small-cased-conversational Conversational DistilRuBERT-small \(Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\] (as [Conversational RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational)). It can be considered as small copy of [Conversational DistilRuBERT-base](https://huggingface.co/DeepPavlov/distilrubert-base-cased-conversational). Our DistilRuBERT-small was highly inspired by \[3\], \[4\]. Namely, we used * KL loss (between teacher and student output logits) * MLM loss (between tokens labels and student output logits) * Cosine embedding loss (between averaged six consecutive hidden states from teacher's encoder and one hidden state of the student) * MSE loss (between averaged six consecutive attention maps from teacher's encoder and one attention map of the student) The model was trained for about 80 hrs. on 8 nVIDIA Tesla P100-SXM2.0 16Gb. To evaluate improvements in the inference speed, we ran teacher and student models on random sequences with seq_len=512, batch_size = 16 (for throughput) and batch_size=1 (for latency). All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb. | Model | Size, Mb. | CPU latency, sec.| GPU latency, sec. | CPU throughput, samples/sec. | GPU throughput, samples/sec. | |-------------------------------------------------|------------|------------------|-------------------|------------------------------|------------------------------| | Teacher (RuBERT-base-cased-conversational) | 679 | 0.655 | 0.031 | 0.3754 | 36.4902 | | Student (DistilRuBERT-small-cased-conversational)| 409 | 0.1656 | 0.015 | 0.9692 | 71.3553 | To evaluate model quality, we fine-tuned DistilRuBERT-small on classification, NER and question answering tasks. Scores and archives with fine-tuned models can be found in [DeepPavlov docs](http://docs.deeppavlov.ai/en/master/features/overview.html#models). # Citation If you found the model useful for your research, we are kindly ask to cite [this](https://arxiv.org/abs/2205.02340) paper: ``` @misc{https://doi.org/10.48550/arxiv.2205.02340, doi = {10.48550/ARXIV.2205.02340}, url = {https://arxiv.org/abs/2205.02340}, author = {Kolesnikova, Alina and Kuratov, Yuri and Konovalov, Vasily and Burtsev, Mikhail}, keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Knowledge Distillation of Russian Language Models with Reduction of Vocabulary}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` \[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\) \[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017. \[3\]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. \(2019\). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. \[4\]: <https://github.com/huggingface/transformers/tree/master/examples/research_projects/distillation>

{"language": ["ru"]}

DeepPavlov/distilrubert-tiny-cased-conversational

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #ru #arxiv-2205.02340 #endpoints_compatible #region-us

WARNING: This is 'distilrubert-small-cased-conversational' model uploaded with wrong name. This one is the same as distilrubert-small-cased-conversational. 'distilrubert-tiny-cased-conversational' could be found in distilrubert-tiny-cased-conversational-v1. distilrubert-small-cased-conversational ======================================= Conversational DistilRuBERT-small (Russian, cased, 2‑layer, 768‑hidden, 12‑heads, 107M parameters) was trained on OpenSubtitles[1], Dirty, Pikabu, and a Social Media segment of Taiga corpus[2] (as Conversational RuBERT). It can be considered as small copy of Conversational DistilRuBERT-base. Our DistilRuBERT-small was highly inspired by [3], [4]. Namely, we used * KL loss (between teacher and student output logits) * MLM loss (between tokens labels and student output logits) * Cosine embedding loss (between averaged six consecutive hidden states from teacher's encoder and one hidden state of the student) * MSE loss (between averaged six consecutive attention maps from teacher's encoder and one attention map of the student) The model was trained for about 80 hrs. on 8 nVIDIA Tesla P100-SXM2.0 16Gb. To evaluate improvements in the inference speed, we ran teacher and student models on random sequences with seq\_len=512, batch\_size = 16 (for throughput) and batch\_size=1 (for latency). All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA Tesla P100-SXM2.0 16Gb. To evaluate model quality, we fine-tuned DistilRuBERT-small on classification, NER and question answering tasks. Scores and archives with fine-tuned models can be found in DeepPavlov docs. If you found the model useful for your research, we are kindly ask to cite this paper: [1]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016) [2]: Shavrina T., Shapovalova O. (2017) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017. [3]: Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108. [4]: <URL

text-classification

transformers

# RoBERTa Large model fine-tuned on Winogrande This model was fine-tuned on Winogrande dataset (XL size) in sequence classification task format, meaning that original pairs of sentences with corresponding options filled in were separated, shuffled and classified independently of each other. ## Model description ## Intended use & limitations ### How to use ## Training data [WinoGrande-XL](https://huggingface.co/datasets/winogrande) reformatted the following way: 1. Each sentence was split on "`_`" placeholder symbol. 2. Each option was concatenated with the second part of the split, thus transforming each example into two text segment pairs. 3. Text segment pairs corresponding to correct and incorrect options were marked with `True` and `False` labels accordingly. 4. Text segment pairs were shuffled thereafter. For example, ```json { "answer": "2", "option1": "plant", "option2": "urn", "sentence": "The plant took up too much room in the urn, because the _ was small." } ``` becomes ```json { "sentence1": "The plant took up too much room in the urn, because the ", "sentence2": "plant was small.", "label": false } ``` and ```json { "sentence1": "The plant took up too much room in the urn, because the ", "sentence2": "urn was small.", "label": true } ``` These sentence pairs are then treated as independent examples. ### BibTeX entry and citation info ```bibtex @article{sakaguchi2019winogrande, title={WinoGrande: An Adversarial Winograd Schema Challenge at Scale}, author={Sakaguchi, Keisuke and Bras, Ronan Le and Bhagavatula, Chandra and Choi, Yejin}, journal={arXiv preprint arXiv:1907.10641}, year={2019} } @article{DBLP:journals/corr/abs-1907-11692, author = {Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov}, title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach}, journal = {CoRR}, volume = {abs/1907.11692}, year = {2019}, url = {http://arxiv.org/abs/1907.11692}, archivePrefix = {arXiv}, eprint = {1907.11692}, timestamp = {Thu, 01 Aug 2019 08:59:33 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

{"language": ["en"], "datasets": ["winogrande"], "widget": [{"text": "The roof of Rachel's home is old and falling apart, while Betty's is new. The home value of </s> Rachel is lower."}, {"text": "The wooden doors at my friends work are worse than the wooden desks at my work, because the </s> desks material is cheaper."}, {"text": "Postal Service were to reduce delivery frequency. </s> The postal service could deliver less frequently."}, {"text": "I put the cake away in the refrigerator. It has a lot of butter in it. </s> The cake has a lot of butter in it."}]}

DeepPavlov/roberta-large-winogrande

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #text-classification #en #dataset-winogrande #arxiv-1907.11692 #autotrain_compatible #endpoints_compatible #region-us

# RoBERTa Large model fine-tuned on Winogrande This model was fine-tuned on Winogrande dataset (XL size) in sequence classification task format, meaning that original pairs of sentences with corresponding options filled in were separated, shuffled and classified independently of each other. ## Model description ## Intended use & limitations ### How to use ## Training data WinoGrande-XL reformatted the following way: 1. Each sentence was split on "'_'" placeholder symbol. 2. Each option was concatenated with the second part of the split, thus transforming each example into two text segment pairs. 3. Text segment pairs corresponding to correct and incorrect options were marked with 'True' and 'False' labels accordingly. 4. Text segment pairs were shuffled thereafter. For example, becomes and These sentence pairs are then treated as independent examples. ### BibTeX entry and citation info

feature-extraction

transformers

# rubert-base-cased-conversational Conversational RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\]. We assembled a new vocabulary for Conversational RuBERT model on this data and initialized the model with [RuBERT](../rubert-base-cased). 08.11.2021: upload model with MLM and NSP heads \[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\) \[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.

{"language": ["ru"]}

DeepPavlov/rubert-base-cased-conversational

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #feature-extraction #ru #endpoints_compatible #has_space #region-us

# rubert-base-cased-conversational Conversational RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on OpenSubtitles\[1\], Dirty, Pikabu, and a Social Media segment of Taiga corpus\[2\]. We assembled a new vocabulary for Conversational RuBERT model on this data and initialized the model with RuBERT. 08.11.2021: upload model with MLM and NSP heads \[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\) \[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.

feature-extraction

transformers

# rubert-base-cased-sentence Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI\[1\] google-translated to russian and on russian part of XNLI dev set\[2\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\[3\]. \[1\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \(2015\) A large annotated corpus for learning natural language inference. arXiv preprint [arXiv:1508.05326](https://arxiv.org/abs/1508.05326) \[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053) \[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)

{"language": ["ru"]}

DeepPavlov/rubert-base-cased-sentence

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #feature-extraction #ru #arxiv-1508.05326 #arxiv-1809.05053 #arxiv-1908.10084 #endpoints_compatible #has_space #region-us

# rubert-base-cased-sentence Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI\[1\] google-translated to russian and on russian part of XNLI dev set\[2\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\[3\]. \[1\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \(2015\) A large annotated corpus for learning natural language inference. arXiv preprint arXiv:1508.05326 \[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint arXiv:1809.05053 \[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint arXiv:1908.10084

feature-extraction

transformers

# rubert-base-cased RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT\[1\]. 08.11.2021: upload model with MLM and NSP heads \[1\]: Kuratov, Y., Arkhipov, M. \(2019\). Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language. arXiv preprint [arXiv:1905.07213](https://arxiv.org/abs/1905.07213).

{"language": ["ru"]}

DeepPavlov/rubert-base-cased

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #feature-extraction #ru #arxiv-1905.07213 #endpoints_compatible #has_space #region-us

# rubert-base-cased RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT\[1\]. 08.11.2021: upload model with MLM and NSP heads \[1\]: Kuratov, Y., Arkhipov, M. \(2019\). Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language. arXiv preprint arXiv:1905.07213.

text-classification

transformers

# XLM-RoBERTa-Large-En-Ru-MNLI xlm-roberta-large-en-ru finetuned on mnli.

{"language": ["en", "ru"], "tags": ["xlm-roberta", "xlm-roberta-large", "xlm-roberta-large-en-ru", "xlm-roberta-large-en-ru-mnli"], "datasets": ["glue", "mnli"], "model_index": [{"name": "mnli", "results": [{"task": {"name": "Text Classification", "type": "text-classification"}, "dataset": {"name": "GLUE MNLI", "type": "glue", "args": "mnli"}}]}], "widget": [{"text": "\u041b\u044e\u0431\u043b\u044e \u0442\u0435\u0431\u044f. \u041d\u0435\u043d\u0430\u0432\u0438\u0436\u0443 \u0442\u0435\u0431\u044f"}, {"text": "I love you. I hate you"}]}

DeepPavlov/xlm-roberta-large-en-ru-mnli

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #text-classification #xlm-roberta-large #xlm-roberta-large-en-ru #xlm-roberta-large-en-ru-mnli #en #ru #dataset-glue #dataset-mnli #autotrain_compatible #endpoints_compatible #has_space #region-us

# XLM-RoBERTa-Large-En-Ru-MNLI xlm-roberta-large-en-ru finetuned on mnli.

feature-extraction

transformers

# XLM-RoBERTa-Large-En-Ru ## Model description This model is a version XLM-RoBERTa with embeddings and vocabulary reduced to most frequent tokens in English and Russian.

{"language": ["en", "ru"]}

DeepPavlov/xlm-roberta-large-en-ru

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #feature-extraction #en #ru #endpoints_compatible #region-us

# XLM-RoBERTa-Large-En-Ru ## Model description This model is a version XLM-RoBERTa with embeddings and vocabulary reduced to most frequent tokens in English and Russian.

automatic-speech-recognition

transformers

# Wav2Vec2-Large-XLSR-53-Lithuanian Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Lithuanian using the [Common Voice](https://huggingface.co/datasets/common_voice) When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "lt", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") model = Wav2Vec2ForCTC.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): \\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): \\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) ``` ## Evaluation The model can be evaluated as follows on the Lithuanian test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "lt", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") model = Wav2Vec2ForCTC.from_pretrained("DeividasM/wav2vec2-large-xlsr-53-lithuanian") model.to("cuda") chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): \\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() \\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\tbatch["speech"] = resampler(speech_array).squeeze().numpy() \\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the audio files as arrays def evaluate(batch): \\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) \\twith torch.no_grad(): \\t\\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) \\tbatch["pred_strings"] = processor.batch_decode(pred_ids) \\treturn batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 56.55 % ## Training The Common Voice `train`, `validation` datasets were used for training.

{"language": "lt", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "model-index": [{"name": "XLSR Wav2Vec2 Lithuanina by Deividas Mataciunas", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice lt", "type": "common_voice", "args": "lt"}, "metrics": [{"type": "wer", "value": 56.55, "name": "Test WER"}]}]}]}

DeividasM/wav2vec2-large-xlsr-53-lithuanian

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #xlsr-fine-tuning-week #lt #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us

# Wav2Vec2-Large-XLSR-53-Lithuanian Fine-tuned facebook/wav2vec2-large-xlsr-53 in Lithuanian using the Common Voice When using this model, make sure that your speech input is sampled at 16kHz. ## Usage The model can be used directly (without a language model) as follows: ## Evaluation The model can be evaluated as follows on the Lithuanian test data of Common Voice. Test Result: 56.55 % ## Training The Common Voice 'train', 'validation' datasets were used for training.

transformers

Need to work with OpenDelta ``` from transformers import AutoModelForSeq2SeqLM t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-base") from opendelta import AutoDeltaModel delta = AutoDeltaModel.from_finetuned("DeltaHub/lora_t5-base_mrpc", backbone_model=t5) delta.log() ```

{}

DeltaHub/lora_t5-base_mrpc

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #endpoints_compatible #region-us

Need to work with OpenDelta

text-classification

transformers

# Modèle de détection de 4 sentiments avec FlauBERT (mixed, negative, objective, positive) ### Comment l'utiliser ? ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import pipeline loaded_tokenizer = AutoTokenizer.from_pretrained('flaubert/flaubert_large_cased') loaded_model = AutoModelForSequenceClassification.from_pretrained("DemangeJeremy/4-sentiments-with-flaubert") nlp = pipeline('sentiment-analysis', model=loaded_model, tokenizer=loaded_tokenizer) print(nlp("Je suis plutôt confiant.")) ``` ``` [{'label': 'OBJECTIVE', 'score': 0.3320835530757904}] ``` ## Résultats de l'évaluation du modèle | Epoch | Validation Loss | Samples Per Second | |:------:|:--------------:|:------------------:| | 1 | 2.219246 | 49.476000 | | 2 | 1.883753 | 47.259000 | | 3 | 1.747969 | 44.957000 | | 4 | 1.695606 | 43.872000 | | 5 | 1.641470 | 45.726000 | ## Citation Pour toute utilisation de ce modèle, merci d'utiliser cette citation : > Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, <https://huggingface.co/DemangeJeremy/4-sentiments-with-flaubert>

{"language": "fr", "tags": ["sentiments", "text-classification", "flaubert", "french", "flaubert-large"]}

DemangeJeremy/4-sentiments-with-flaubert

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #flaubert #text-classification #sentiments #french #flaubert-large #fr #autotrain_compatible #endpoints_compatible #region-us

Modèle de détection de 4 sentiments avec FlauBERT (mixed, negative, objective, positive) ======================================================================================== ### Comment l'utiliser ? Résultats de l'évaluation du modèle ----------------------------------- Pour toute utilisation de ce modèle, merci d'utiliser cette citation : > > Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, <URL > > >

text-generation

transformers

# Asuna Yuuki DialoGPT Model

{"tags": ["conversational"]}

Denny29/DialoGPT-medium-asunayuuki

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Asuna Yuuki DialoGPT Model

title: ArcaneGAN emoji: 🚀 colorFrom: blue colorTo: blue sdk: gradio app_file: app.py pinned: false

{}

Despin89/test

2022-03-02T23:29:04+00:00

TAGS #region-us

title: ArcaneGAN emoji: colorFrom: blue colorTo: blue sdk: gradio app_file: URL pinned: false

token-classification

transformers

# Token classification for FOODs. Detects foods in sentences. Currently, only supports spanish. Multiple words foods are detected as one entity. ## To-do - English support. - Negation support. - Quantity tags. - Psychosocial tags.

{"widget": [{"text": "El paciente se alimenta de pan, sopa de calabaza y coca-cola"}]}

Dev-DGT/food-dbert-multiling

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #distilbert #token-classification #autotrain_compatible #endpoints_compatible #region-us

# Token classification for FOODs. Detects foods in sentences. Currently, only supports spanish. Multiple words foods are detected as one entity. ## To-do - English support. - Negation support. - Quantity tags. - Psychosocial tags.

text-generation

transformers

# Miku DialogGPT Model

{"tags": ["conversational"]}

Devid/DialoGPT-small-Miku

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Miku DialogGPT Model

The default Prism model available at https://github.com/thompsonb/prism. See the [README.md](https://github.com/thompsonb/prism/blob/master/README.md) file for more information. **LICENCE NOTICE** ``` MIT License Copyright (c) Brian Thompson Portions of this software are copied from fairseq (https://github.com/pytorch/fairseq), which is released under the MIT License and Copyright (c) Facebook, Inc. and its affiliates. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ```

{"license": "mit"}

Devrim/prism-default

2022-03-02T23:29:04+00:00

TAGS #license-mit #region-us

The default Prism model available at URL See the URL file for more information. LICENCE NOTICE

Hello

{}

DevsIA/imagenes

2022-03-02T23:29:04+00:00

TAGS #region-us

Hello

automatic-speech-recognition

# Wav2Vec2-Large-XLSR-Welsh This model has moved to https://huggingface.co/techiaith/wav2vec2-xlsr-ft-cy

{"language": "cy", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech", "xlsr-fine-tuning-week"], "datasets": ["common_voice"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xlsr-welsh (by Dewi Bryn Jones, fine tuning week - March 2021)", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice cy", "type": "common_voice", "args": "cy"}, "metrics": [{"type": "wer", "value": "25.59%", "name": "Test WER"}]}]}]}

DewiBrynJones/wav2vec2-large-xlsr-welsh

2022-03-02T23:29:04+00:00

TAGS #audio #automatic-speech-recognition #speech #xlsr-fine-tuning-week #cy #dataset-common_voice #license-apache-2.0 #model-index #region-us

# Wav2Vec2-Large-XLSR-Welsh This model has moved to URL

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-en-ro-finetuned-en-to-ro This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2915 - Bleu: 27.9273 - Gen Len: 34.0935 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 0.7448 | 1.0 | 38145 | 1.2915 | 27.9273 | 34.0935 | ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wmt16"], "metrics": ["bleu"], "model-index": [{"name": "opus-mt-en-ro-finetuned-en-to-ro", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "wmt16", "type": "wmt16", "args": "ro-en"}, "metrics": [{"type": "bleu", "value": 27.9273, "name": "Bleu"}]}]}]}

DiegoAlysson/opus-mt-en-ro-finetuned-en-to-ro

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #marian #text2text-generation #generated_from_trainer #dataset-wmt16 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

opus-mt-en-ro-finetuned-en-to-ro ================================ This model is a fine-tuned version of Helsinki-NLP/opus-mt-en-ro on the wmt16 dataset. It achieves the following results on the evaluation set: * Loss: 1.2915 * Bleu: 27.9273 * Gen Len: 34.0935 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.12.5 * Pytorch 1.10.0+cu111 * Datasets 1.15.1 * Tokenizers 0.10.3

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

Dilmk2/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Harry Potter DialoGPT Model

text-generation

transformers

# V DialoGPT Model

{"tags": ["conversational"]}

Dimedrolza/DialoGPT-small-cyberpunk

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# V DialoGPT Model

text-generation

transformers

# HomerBot: A conversational chatbot imitating Homer Simpson This model is a fine-tuned [DialoGPT](https://huggingface.co/microsoft/DialoGPT-medium) (medium version) on Simpsons [scripts](https://www.kaggle.com/datasets/pierremegret/dialogue-lines-of-the-simpsons). More specifically, we fine-tune DialoGPT-medium for 3 epochs on 10K **(character utterance, Homer's response)** pairs For more details, check out our git [repo](https://github.com/jesseDingley/HomerBot) containing all the code. ### How to use ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("DingleyMaillotUrgell/homer-bot") model = AutoModelForCausalLM.from_pretrained("DingleyMaillotUrgell/homer-bot") # Let's chat for 5 lines for step in range(5): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User: ") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate( bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature = 0.8 ) # print last outpput tokens from bot print("Homer: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```

{"language": ["en"], "tags": ["conversational"]}

jesseD/homer-bot

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #en #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# HomerBot: A conversational chatbot imitating Homer Simpson This model is a fine-tuned DialoGPT (medium version) on Simpsons scripts. More specifically, we fine-tune DialoGPT-medium for 3 epochs on 10K (character utterance, Homer's response) pairs For more details, check out our git repo containing all the code. ### How to use

text-generation

transformers

# Harry Potter DialoGPT Medium Model

{"tags": ["conversational"]}

Doiman/DialoGPT-medium-harrypotter

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Harry Potter DialoGPT Medium Model

text-generation

transformers

# Rick DialoGPT Model

{"tags": ["conversational"]}

DongHai/DialoGPT-small-rick

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Rick DialoGPT Model

text-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7335 - Matthews Correlation: 0.5356 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | |:-------------:|:-----:|:----:|:---------------:|:--------------------:| | 0.5309 | 1.0 | 535 | 0.5070 | 0.4239 | | 0.3568 | 2.0 | 1070 | 0.5132 | 0.4913 | | 0.24 | 3.0 | 1605 | 0.6081 | 0.4990 | | 0.1781 | 4.0 | 2140 | 0.7335 | 0.5356 | | 0.1243 | 5.0 | 2675 | 0.8705 | 0.5242 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["matthews_correlation"], "model-index": [{"name": "distilbert-base-uncased-finetuned-cola", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "cola"}, "metrics": [{"type": "matthews_correlation", "value": 0.535587402888147, "name": "Matthews Correlation"}]}]}]}

DongHyoungLee/distilbert-base-uncased-finetuned-cola

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-cola ====================================== This model is a fine-tuned version of distilbert-base-uncased on the glue dataset. It achieves the following results on the evaluation set: * Loss: 0.7335 * Matthews Correlation: 0.5356 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.9.0+cu111 * Datasets 1.13.3 * Tokenizers 0.10.3

question-answering

transformers

The Reader model is for Korean Question Answering The backbone model is deepset/xlm-roberta-large-squad2. It is a finetuned model with KorQuAD-v1 dataset. As a result of verification using KorQuAD evaluation dataset, it showed approximately 87% and 92% respectively for the EM score and F1 score. Thank you

{}

Dongjae/mrc2reader

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #question-answering #endpoints_compatible #region-us

The Reader model is for Korean Question Answering The backbone model is deepset/xlm-roberta-large-squad2. It is a finetuned model with KorQuAD-v1 dataset. As a result of verification using KorQuAD evaluation dataset, it showed approximately 87% and 92% respectively for the EM score and F1 score. Thank you

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Wayne_NLP_mT5 This model was trained only english datasets. if you want trained korean + english model go to wayne_mulang_mT5. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0a0+3fd9dcf - Datasets 1.18.3 - Tokenizers 0.11.0

{"tags": ["generated_from_trainer"], "datasets": ["cnn_dailymail"], "model-index": [{"name": "Wayne_NLP_mT5", "results": []}]}

Waynehillsdev/Wayne_NLP_mT5

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #dataset-cnn_dailymail #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Wayne_NLP_mT5 This model was trained only english datasets. if you want trained korean + english model go to wayne_mulang_mT5. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0a0+3fd9dcf - Datasets 1.18.3 - Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Waynehills-STT-doogie-server This model is a fine-tuned version of [Doogie/Waynehills-STT-doogie-server](https://huggingface.co/Doogie/Waynehills-STT-doogie-server) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 60 ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"]}

Waynehillsdev/Waynehills-STT-doogie-server

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us

# Waynehills-STT-doogie-server This model is a fine-tuned version of Doogie/Waynehills-STT-doogie-server on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 60 ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Waynehills_summary_tensorflow This model is a fine-tuned version of [KETI-AIR/ke-t5-base-ko](https://huggingface.co/KETI-AIR/ke-t5-base-ko) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "Waynehills_summary_tensorflow", "results": []}]}

Waynehillsdev/Waynehills_summary_tensorflow

2022-03-02T23:29:04+00:00

TAGS #transformers #tf #t5 #text2text-generation #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Waynehills_summary_tensorflow This model is a fine-tuned version of KETI-AIR/ke-t5-base-ko on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4180 - Wer: 0.3392 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.656 | 4.0 | 500 | 1.8973 | 1.0130 | | 0.8647 | 8.0 | 1000 | 0.4667 | 0.4705 | | 0.2968 | 12.0 | 1500 | 0.4211 | 0.4035 | | 0.1719 | 16.0 | 2000 | 0.4725 | 0.3739 | | 0.1272 | 20.0 | 2500 | 0.4586 | 0.3543 | | 0.1079 | 24.0 | 3000 | 0.4356 | 0.3484 | | 0.0808 | 28.0 | 3500 | 0.4180 | 0.3392 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.9.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-timit-demo-colab", "results": []}]}

Waynehillsdev/wav2vec2-base-timit-demo-colab

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us

wav2vec2-base-timit-demo-colab ============================== This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.4180 * Wer: 0.3392 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 30 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.11.3 * Pytorch 1.9.0+cu111 * Datasets 1.13.3 * Tokenizers 0.10.3

question-answering

transformers

Model for Extraction-based MRC original model : klue/roberta-large Designed for ODQA Competition

{}

Doohae/roberta

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #roberta #question-answering #endpoints_compatible #region-us

Model for Extraction-based MRC original model : klue/roberta-large Designed for ODQA Competition

text-generation

transformers

#Rick DialoGPT model

{"tags": ["conversational"]}

Doquey/DialoGPT-small-Luisbot1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#Rick DialoGPT model

text-generation

transformers

#Michael

{"tags": "conversational"}

Doquey/DialoGPT-small-Michaelbot

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#Michael

text-generation

transformers

# Celestia Ludenburg DiabloGPT Model

{"tags": ["conversational"]}

Doxophobia/DialoGPT-medium-celeste

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Celestia Ludenburg DiabloGPT Model

text-classification

transformers

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # tmp_qubhe07 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1374, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3

{"tags": ["generated_from_keras_callback"], "model-index": [{"name": "tmp_qubhe07", "results": []}]}

DoyyingFace/doyying_bert_first_again

2022-03-02T23:29:04+00:00

TAGS #transformers #tf #bert #text-classification #generated_from_keras_callback #autotrain_compatible #endpoints_compatible #region-us

# tmp_qubhe07 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1374, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3

fill-mask

transformers

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # dummy-model This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_keras_callback"], "model-index": [{"name": "dummy-model", "results": []}]}

DoyyingFace/dummy-model

2022-03-02T23:29:04+00:00

TAGS #transformers #tf #camembert #fill-mask #generated_from_keras_callback #license-mit #autotrain_compatible #endpoints_compatible #region-us

# dummy-model This model is a fine-tuned version of camembert-base on an unknown dataset. It achieves the following results on the evaluation set: ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.15.0 - TensorFlow 2.7.0 - Datasets 1.17.0 - Tokenizers 0.10.3

text-generation

transformers

# Legacies DialoGPT Model

{"tags": ["conversational"]}

Dragoniod1596/DialoGPT-small-Legacies

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Legacies DialoGPT Model

text-generation

transformers

#Uncle Iroh DialoGPT Model

{"tags": ["conversational"]}

Dreyzin/DialoGPT-medium-avatar

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#Uncle Iroh DialoGPT Model

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset. It achieves the following results on the evaluation set: - Loss: 0.5620 - Wer: 0.5651 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-ab-CV7 --dataset mozilla-foundation/common_voice_7_0 --config ab --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data NA ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.6445 | 13.64 | 300 | 4.3963 | 1.0 | | 3.6459 | 27.27 | 600 | 3.2267 | 1.0 | | 3.0978 | 40.91 | 900 | 3.0927 | 1.0 | | 2.8357 | 54.55 | 1200 | 2.1462 | 1.0029 | | 1.2723 | 68.18 | 1500 | 0.6747 | 0.6996 | | 0.6528 | 81.82 | 1800 | 0.5928 | 0.6422 | | 0.4905 | 95.45 | 2100 | 0.5587 | 0.5681 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0

{"language": ["ab"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "ab", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-ab-CV7", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "ab"}, "metrics": [{"type": "wer", "value": 0.5291160452450775, "name": "Test WER"}, {"type": "cer", "value": 0.10630270750110964, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "ab"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-ab-CV7

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - AB dataset. It achieves the following results on the evaluation set: * Loss: 0.5620 * Wer: 0.5651 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-ab-CV7 --dataset mozilla-foundation/common\_voice\_7\_0 --config ab --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data NA ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7.5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 2000 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.1+cu102 * Datasets 1.17.1.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - AB dataset. It achieves the following results on the evaluation set: - Loss: 0.6178 - Wer: 0.5794 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00025 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 70.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 5.2793 | 27.27 | 300 | 3.0737 | 1.0 | | 1.5348 | 54.55 | 600 | 0.6312 | 0.6334 | ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0

{"language": ["ab"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-ab-v4

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #ab #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - AB dataset. It achieves the following results on the evaluation set: * Loss: 0.6178 * Wer: 0.5794 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.00025 * train\_batch\_size: 32 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 70.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.0.dev0 * Pytorch 1.10.1+cu102 * Datasets 1.17.1.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-as-g1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - AS dataset. It achieves the following results on the evaluation set: - Loss: 1.3327 - Wer: 0.5744 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-as-g1 --dataset mozilla-foundation/common_voice_8_0 --config as --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Assamese language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 14.1958 | 5.26 | 100 | 7.1919 | 1.0 | | 5.0035 | 10.51 | 200 | 3.9362 | 1.0 | | 3.6193 | 15.77 | 300 | 3.4451 | 1.0 | | 3.4852 | 21.05 | 400 | 3.3536 | 1.0 | | 2.8489 | 26.31 | 500 | 1.6451 | 0.9100 | | 0.9568 | 31.56 | 600 | 1.0514 | 0.7561 | | 0.4865 | 36.82 | 700 | 1.0434 | 0.7184 | | 0.322 | 42.1 | 800 | 1.0825 | 0.7210 | | 0.2383 | 47.36 | 900 | 1.1304 | 0.6897 | | 0.2136 | 52.62 | 1000 | 1.1150 | 0.6854 | | 0.179 | 57.87 | 1100 | 1.2453 | 0.6875 | | 0.1539 | 63.15 | 1200 | 1.2211 | 0.6704 | | 0.1303 | 68.41 | 1300 | 1.2859 | 0.6747 | | 0.1183 | 73.67 | 1400 | 1.2775 | 0.6721 | | 0.0994 | 78.92 | 1500 | 1.2321 | 0.6404 | | 0.0991 | 84.21 | 1600 | 1.2766 | 0.6524 | | 0.0887 | 89.46 | 1700 | 1.3026 | 0.6344 | | 0.0754 | 94.72 | 1800 | 1.3199 | 0.6704 | | 0.0693 | 99.97 | 1900 | 1.3044 | 0.6361 | | 0.0568 | 105.26 | 2000 | 1.3541 | 0.6254 | | 0.0536 | 110.51 | 2100 | 1.3320 | 0.6249 | | 0.0529 | 115.77 | 2200 | 1.3370 | 0.6271 | | 0.048 | 121.05 | 2300 | 1.2757 | 0.6031 | | 0.0419 | 126.31 | 2400 | 1.2661 | 0.6172 | | 0.0349 | 131.56 | 2500 | 1.2897 | 0.6048 | | 0.0309 | 136.82 | 2600 | 1.2688 | 0.5962 | | 0.0278 | 142.1 | 2700 | 1.2885 | 0.5954 | | 0.0254 | 147.36 | 2800 | 1.2988 | 0.5915 | | 0.0223 | 152.62 | 2900 | 1.3153 | 0.5941 | | 0.0216 | 157.87 | 3000 | 1.2936 | 0.5937 | | 0.0186 | 163.15 | 3100 | 1.2906 | 0.5877 | | 0.0156 | 168.41 | 3200 | 1.3476 | 0.5962 | | 0.0158 | 173.67 | 3300 | 1.3363 | 0.5847 | | 0.0142 | 178.92 | 3400 | 1.3367 | 0.5847 | | 0.0153 | 184.21 | 3500 | 1.3105 | 0.5757 | | 0.0119 | 189.46 | 3600 | 1.3255 | 0.5705 | | 0.0115 | 194.72 | 3700 | 1.3340 | 0.5787 | | 0.0103 | 199.97 | 3800 | 1.3327 | 0.5744 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["as"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "as", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-as-g1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "as"}, "metrics": [{"type": "wer", "value": 0.6540934419202743, "name": "Test WER"}, {"type": "cer", "value": 0.21454042646095625, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "as"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-as-g1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #as #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-as-g1 =============================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - AS dataset. It achieves the following results on the evaluation set: * Loss: 1.3327 * Wer: 0.5744 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-as-g1 --dataset mozilla-foundation/common\_voice\_8\_0 --config as --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Assamese language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0003 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-as-v9 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.1679 - Wer: 0.5761 ### Evaluation Command 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-as-v9 --dataset mozilla-foundation/common_voice_8_0 --config as --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Assamese (as) language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000111 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 8.3852 | 10.51 | 200 | 3.6402 | 1.0 | | 3.5374 | 21.05 | 400 | 3.3894 | 1.0 | | 2.8645 | 31.56 | 600 | 1.3143 | 0.8303 | | 1.1784 | 42.1 | 800 | 0.9417 | 0.6661 | | 0.7805 | 52.62 | 1000 | 0.9292 | 0.6237 | | 0.5973 | 63.15 | 1200 | 0.9489 | 0.6014 | | 0.4784 | 73.67 | 1400 | 0.9916 | 0.5962 | | 0.4138 | 84.21 | 1600 | 1.0272 | 0.6121 | | 0.3491 | 94.72 | 1800 | 1.0412 | 0.5984 | | 0.3062 | 105.26 | 2000 | 1.0769 | 0.6005 | | 0.2707 | 115.77 | 2200 | 1.0708 | 0.5752 | | 0.2459 | 126.31 | 2400 | 1.1285 | 0.6009 | | 0.2234 | 136.82 | 2600 | 1.1209 | 0.5949 | | 0.2035 | 147.36 | 2800 | 1.1348 | 0.5842 | | 0.1876 | 157.87 | 3000 | 1.1480 | 0.5872 | | 0.1669 | 168.41 | 3200 | 1.1496 | 0.5838 | | 0.1595 | 178.92 | 3400 | 1.1721 | 0.5778 | | 0.1505 | 189.46 | 3600 | 1.1654 | 0.5744 | | 0.1486 | 199.97 | 3800 | 1.1679 | 0.5761 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"language": ["as"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "as", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-as-v9", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "hsb"}, "metrics": [{"type": "wer", "value": 0.6163737676810973, "name": "Test WER"}, {"type": "cer", "value": 0.19496397642093005, "name": "Test CER"}, {"type": "wer", "value": 61.64, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "as"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-as-v9

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #as #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-as-v9 =============================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset. It achieves the following results on the evaluation set: * Loss: 1.1679 * Wer: 0.5761 ### Evaluation Command 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-as-v9 --dataset mozilla-foundation/common\_voice\_8\_0 --config as --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Assamese (as) language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000111 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 300 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.1 * Pytorch 1.10.0+cu111 * Datasets 1.18.2 * Tokenizers 0.11.0

automatic-speech-recognition

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> ### Note: Files are missing. Probably, didn't get (git)pushed properly. :( This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.1679 - Wer: 0.5761 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000111 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 8.3852 | 10.51 | 200 | 3.6402 | 1.0 | | 3.5374 | 21.05 | 400 | 3.3894 | 1.0 | | 2.8645 | 31.56 | 600 | 1.3143 | 0.8303 | | 1.1784 | 42.1 | 800 | 0.9417 | 0.6661 | | 0.7805 | 52.62 | 1000 | 0.9292 | 0.6237 | | 0.5973 | 63.15 | 1200 | 0.9489 | 0.6014 | | 0.4784 | 73.67 | 1400 | 0.9916 | 0.5962 | | 0.4138 | 84.21 | 1600 | 1.0272 | 0.6121 | | 0.3491 | 94.72 | 1800 | 1.0412 | 0.5984 | | 0.3062 | 105.26 | 2000 | 1.0769 | 0.6005 | | 0.2707 | 115.77 | 2200 | 1.0708 | 0.5752 | | 0.2459 | 126.31 | 2400 | 1.1285 | 0.6009 | | 0.2234 | 136.82 | 2600 | 1.1209 | 0.5949 | | 0.2035 | 147.36 | 2800 | 1.1348 | 0.5842 | | 0.1876 | 157.87 | 3000 | 1.1480 | 0.5872 | | 0.1669 | 168.41 | 3200 | 1.1496 | 0.5838 | | 0.1595 | 178.92 | 3400 | 1.1721 | 0.5778 | | 0.1505 | 189.46 | 3600 | 1.1654 | 0.5744 | | 0.1486 | 199.97 | 3800 | 1.1679 | 0.5761 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"language": ["as"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "as", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-as-with-LM-v2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "hsb"}, "metrics": [{"type": "wer", "value": [], "name": "Test WER"}, {"type": "cer", "value": [], "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-as-with-LM-v2

2022-03-02T23:29:04+00:00

TAGS #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #as #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-common_voice #license-apache-2.0 #model-index #region-us

### Note: Files are missing. Probably, didn't get (git)pushed properly. :( This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset. It achieves the following results on the evaluation set: * Loss: 1.1679 * Wer: 0.5761 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000111 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 300 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.1 * Pytorch 1.10.0+cu111 * Datasets 1.18.2 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-bas-v1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BAS dataset. It achieves the following results on the evaluation set: - Loss: 0.5997 - Wer: 0.3870 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bas-v1 --dataset mozilla-foundation/common_voice_8_0 --config bas --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Basaa (bas) language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000111 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 12.7076 | 5.26 | 200 | 3.6361 | 1.0 | | 3.1657 | 10.52 | 400 | 3.0101 | 1.0 | | 2.3987 | 15.78 | 600 | 0.9125 | 0.6774 | | 1.0079 | 21.05 | 800 | 0.6477 | 0.5352 | | 0.7392 | 26.31 | 1000 | 0.5432 | 0.4929 | | 0.6114 | 31.57 | 1200 | 0.5498 | 0.4639 | | 0.5222 | 36.83 | 1400 | 0.5220 | 0.4561 | | 0.4648 | 42.1 | 1600 | 0.5586 | 0.4289 | | 0.4103 | 47.36 | 1800 | 0.5337 | 0.4082 | | 0.3692 | 52.62 | 2000 | 0.5421 | 0.3861 | | 0.3403 | 57.88 | 2200 | 0.5549 | 0.4096 | | 0.3011 | 63.16 | 2400 | 0.5833 | 0.3925 | | 0.2932 | 68.42 | 2600 | 0.5674 | 0.3815 | | 0.2696 | 73.68 | 2800 | 0.5734 | 0.3889 | | 0.2496 | 78.94 | 3000 | 0.5968 | 0.3985 | | 0.2289 | 84.21 | 3200 | 0.5888 | 0.3893 | | 0.2091 | 89.47 | 3400 | 0.5849 | 0.3852 | | 0.2005 | 94.73 | 3600 | 0.5938 | 0.3875 | | 0.1876 | 99.99 | 3800 | 0.5997 | 0.3870 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"language": ["bas"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "bas", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bas-v1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "bas"}, "metrics": [{"type": "wer", "value": 0.3566497929130234, "name": "Test WER"}, {"type": "cer", "value": 0.1102657634184471, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "bas"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-bas-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #bas #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-bas-v1 ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - BAS dataset. It achieves the following results on the evaluation set: * Loss: 0.5997 * Wer: 0.3870 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-bas-v1 --dataset mozilla-foundation/common\_voice\_8\_0 --config bas --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Basaa (bas) language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000111 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 100 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.1 * Pytorch 1.10.0+cu111 * Datasets 1.18.2 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-bg-d2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset. It achieves the following results on the evaluation set: - Loss: 0.3421 - Wer: 0.2860 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset mozilla-foundation/common_voice_8_0 --config bg --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 10 --stride_length_s 1 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00025 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 700 - num_epochs: 35 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.8791 | 1.74 | 200 | 3.1902 | 1.0 | | 3.0441 | 3.48 | 400 | 2.8098 | 0.9864 | | 1.1499 | 5.22 | 600 | 0.4668 | 0.5014 | | 0.4968 | 6.96 | 800 | 0.4162 | 0.4472 | | 0.3553 | 8.7 | 1000 | 0.3580 | 0.3777 | | 0.3027 | 10.43 | 1200 | 0.3422 | 0.3506 | | 0.2562 | 12.17 | 1400 | 0.3556 | 0.3639 | | 0.2272 | 13.91 | 1600 | 0.3621 | 0.3583 | | 0.2125 | 15.65 | 1800 | 0.3436 | 0.3358 | | 0.1904 | 17.39 | 2000 | 0.3650 | 0.3545 | | 0.1695 | 19.13 | 2200 | 0.3366 | 0.3241 | | 0.1532 | 20.87 | 2400 | 0.3550 | 0.3311 | | 0.1453 | 22.61 | 2600 | 0.3582 | 0.3131 | | 0.1359 | 24.35 | 2800 | 0.3524 | 0.3084 | | 0.1233 | 26.09 | 3000 | 0.3503 | 0.2973 | | 0.1114 | 27.83 | 3200 | 0.3434 | 0.2946 | | 0.1051 | 29.57 | 3400 | 0.3474 | 0.2956 | | 0.0965 | 31.3 | 3600 | 0.3426 | 0.2907 | | 0.0923 | 33.04 | 3800 | 0.3478 | 0.2894 | | 0.0894 | 34.78 | 4000 | 0.3421 | 0.2860 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["bg"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "bg", "generated_from_trainer", "hf-asr-leaderboard", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bg-d2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "bg"}, "metrics": [{"type": "wer", "value": 0.28775471338792613, "name": "Test WER"}, {"type": "cer", "value": 0.06861971204625049, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "bg"}, "metrics": [{"type": "wer", "value": 0.49783147459727384, "name": "Test WER"}, {"type": "cer", "value": 0.1591062599627158, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "bg"}, "metrics": [{"type": "wer", "value": 51.25, "name": "Test WER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #bg #generated_from_trainer #hf-asr-leaderboard #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-bg-d2 =============================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - BG dataset. It achieves the following results on the evaluation set: * Loss: 0.3421 * Wer: 0.2860 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset mozilla-foundation/common\_voice\_8\_0 --config bg --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-d2 --dataset speech-recognition-community-v2/dev\_data --config bg --split validation --chunk\_length\_s 10 --stride\_length\_s 1 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.00025 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 700 * num\_epochs: 35 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BG dataset. It achieves the following results on the evaluation set: - Loss: 0.5197 - Wer: 0.4689 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-v1 --dataset mozilla-foundation/common_voice_8_0 --config bg --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-v1 --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 10 --stride_length_s 1 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 4.3711 | 2.61 | 300 | 4.3122 | 1.0 | | 3.1653 | 5.22 | 600 | 3.1156 | 1.0 | | 2.8904 | 7.83 | 900 | 2.8421 | 0.9918 | | 0.9207 | 10.43 | 1200 | 0.9895 | 0.8689 | | 0.6384 | 13.04 | 1500 | 0.6994 | 0.7700 | | 0.5215 | 15.65 | 1800 | 0.5628 | 0.6443 | | 0.4573 | 18.26 | 2100 | 0.5316 | 0.6174 | | 0.3875 | 20.87 | 2400 | 0.4932 | 0.5779 | | 0.3562 | 23.48 | 2700 | 0.4972 | 0.5475 | | 0.3218 | 26.09 | 3000 | 0.4895 | 0.5219 | | 0.2954 | 28.7 | 3300 | 0.5226 | 0.5192 | | 0.287 | 31.3 | 3600 | 0.4957 | 0.5146 | | 0.2587 | 33.91 | 3900 | 0.4944 | 0.4893 | | 0.2496 | 36.52 | 4200 | 0.4976 | 0.4895 | | 0.2365 | 39.13 | 4500 | 0.5185 | 0.4819 | | 0.2264 | 41.74 | 4800 | 0.5152 | 0.4776 | | 0.2224 | 44.35 | 5100 | 0.5031 | 0.4746 | | 0.2096 | 46.96 | 5400 | 0.5062 | 0.4708 | | 0.2038 | 49.57 | 5700 | 0.5217 | 0.4698 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["bg"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "bg", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-bg-v1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "bg"}, "metrics": [{"type": "wer", "value": 0.4709579127785184, "name": "Test WER"}, {"type": "cer", "value": 0.10205125354383235, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "bg"}, "metrics": [{"type": "wer", "value": 0.7053128872366791, "name": "Test WER"}, {"type": "cer", "value": 0.210804311998487, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "bg"}, "metrics": [{"type": "wer", "value": 72.6, "name": "Test WER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-bg-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #bg #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - BG dataset. It achieves the following results on the evaluation set: * Loss: 0.5197 * Wer: 0.4689 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-v1 --dataset mozilla-foundation/common\_voice\_8\_0 --config bg --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-bg-v1 --dataset speech-recognition-community-v2/dev\_data --config bg --split validation --chunk\_length\_s 10 --stride\_length\_s 1 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7e-05 * train\_batch\_size: 32 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 2000 * num\_epochs: 50.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-br-d10 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BR dataset. It achieves the following results on the evaluation set: - Loss: 1.1382 - Wer: 0.4895 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-br-d10 --dataset mozilla-foundation/common_voice_8_0 --config br --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Breton language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 13.611 | 0.68 | 100 | 5.8492 | 1.0 | | 3.8176 | 1.35 | 200 | 3.2181 | 1.0 | | 3.0457 | 2.03 | 300 | 3.0902 | 1.0 | | 2.2632 | 2.7 | 400 | 1.4882 | 0.9426 | | 1.1965 | 3.38 | 500 | 1.1396 | 0.7950 | | 0.984 | 4.05 | 600 | 1.0216 | 0.7583 | | 0.8036 | 4.73 | 700 | 1.0258 | 0.7202 | | 0.7061 | 5.41 | 800 | 0.9710 | 0.6820 | | 0.689 | 6.08 | 900 | 0.9731 | 0.6488 | | 0.6063 | 6.76 | 1000 | 0.9442 | 0.6569 | | 0.5215 | 7.43 | 1100 | 1.0221 | 0.6671 | | 0.4965 | 8.11 | 1200 | 0.9266 | 0.6181 | | 0.4321 | 8.78 | 1300 | 0.9050 | 0.5991 | | 0.3762 | 9.46 | 1400 | 0.9801 | 0.6134 | | 0.3747 | 10.14 | 1500 | 0.9210 | 0.5747 | | 0.3554 | 10.81 | 1600 | 0.9720 | 0.6051 | | 0.3148 | 11.49 | 1700 | 0.9672 | 0.6099 | | 0.3176 | 12.16 | 1800 | 1.0120 | 0.5966 | | 0.2915 | 12.84 | 1900 | 0.9490 | 0.5653 | | 0.2696 | 13.51 | 2000 | 0.9394 | 0.5819 | | 0.2569 | 14.19 | 2100 | 1.0197 | 0.5667 | | 0.2395 | 14.86 | 2200 | 0.9771 | 0.5608 | | 0.2367 | 15.54 | 2300 | 1.0516 | 0.5678 | | 0.2153 | 16.22 | 2400 | 1.0097 | 0.5679 | | 0.2092 | 16.89 | 2500 | 1.0143 | 0.5430 | | 0.2046 | 17.57 | 2600 | 1.0884 | 0.5631 | | 0.1937 | 18.24 | 2700 | 1.0113 | 0.5648 | | 0.1752 | 18.92 | 2800 | 1.0056 | 0.5470 | | 0.164 | 19.59 | 2900 | 1.0340 | 0.5508 | | 0.1723 | 20.27 | 3000 | 1.0743 | 0.5615 | | 0.1535 | 20.95 | 3100 | 1.0495 | 0.5465 | | 0.1432 | 21.62 | 3200 | 1.0390 | 0.5333 | | 0.1561 | 22.3 | 3300 | 1.0798 | 0.5590 | | 0.1384 | 22.97 | 3400 | 1.1716 | 0.5449 | | 0.1359 | 23.65 | 3500 | 1.1154 | 0.5420 | | 0.1356 | 24.32 | 3600 | 1.0883 | 0.5387 | | 0.1355 | 25.0 | 3700 | 1.1114 | 0.5504 | | 0.1158 | 25.68 | 3800 | 1.1171 | 0.5388 | | 0.1166 | 26.35 | 3900 | 1.1335 | 0.5403 | | 0.1165 | 27.03 | 4000 | 1.1374 | 0.5248 | | 0.1064 | 27.7 | 4100 | 1.0336 | 0.5298 | | 0.0987 | 28.38 | 4200 | 1.0407 | 0.5216 | | 0.104 | 29.05 | 4300 | 1.1012 | 0.5350 | | 0.0894 | 29.73 | 4400 | 1.1016 | 0.5310 | | 0.0912 | 30.41 | 4500 | 1.1383 | 0.5302 | | 0.0972 | 31.08 | 4600 | 1.0851 | 0.5214 | | 0.0832 | 31.76 | 4700 | 1.1705 | 0.5311 | | 0.0859 | 32.43 | 4800 | 1.0750 | 0.5192 | | 0.0811 | 33.11 | 4900 | 1.0900 | 0.5180 | | 0.0825 | 33.78 | 5000 | 1.1271 | 0.5196 | | 0.07 | 34.46 | 5100 | 1.1289 | 0.5141 | | 0.0689 | 35.14 | 5200 | 1.0960 | 0.5101 | | 0.068 | 35.81 | 5300 | 1.1377 | 0.5050 | | 0.0776 | 36.49 | 5400 | 1.0880 | 0.5194 | | 0.0642 | 37.16 | 5500 | 1.1027 | 0.5076 | | 0.0607 | 37.84 | 5600 | 1.1293 | 0.5119 | | 0.0607 | 38.51 | 5700 | 1.1229 | 0.5103 | | 0.0545 | 39.19 | 5800 | 1.1168 | 0.5103 | | 0.0562 | 39.86 | 5900 | 1.1206 | 0.5073 | | 0.0484 | 40.54 | 6000 | 1.1710 | 0.5019 | | 0.0499 | 41.22 | 6100 | 1.1511 | 0.5100 | | 0.0455 | 41.89 | 6200 | 1.1488 | 0.5009 | | 0.0475 | 42.57 | 6300 | 1.1196 | 0.4944 | | 0.0413 | 43.24 | 6400 | 1.1654 | 0.4996 | | 0.0389 | 43.92 | 6500 | 1.0961 | 0.4930 | | 0.0428 | 44.59 | 6600 | 1.0955 | 0.4938 | | 0.039 | 45.27 | 6700 | 1.1323 | 0.4955 | | 0.0352 | 45.95 | 6800 | 1.1040 | 0.4930 | | 0.0334 | 46.62 | 6900 | 1.1382 | 0.4942 | | 0.0338 | 47.3 | 7000 | 1.1264 | 0.4911 | | 0.0307 | 47.97 | 7100 | 1.1216 | 0.4881 | | 0.0286 | 48.65 | 7200 | 1.1459 | 0.4894 | | 0.0348 | 49.32 | 7300 | 1.1419 | 0.4906 | | 0.0329 | 50.0 | 7400 | 1.1382 | 0.4895 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["br"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-br-d10", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "br"}, "metrics": [{"type": "wer", "value": 0.5230357484228637, "name": "Test WER"}, {"type": "cer", "value": 0.1880661144228536, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "br"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-br-d10

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #br #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-br-d10 ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - BR dataset. It achieves the following results on the evaluation set: * Loss: 1.1382 * Wer: 0.4895 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-br-d10 --dataset mozilla-foundation/common\_voice\_8\_0 --config br --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Breton language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0004 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 800 * num\_epochs: 50 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-br-d2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - BR dataset. It achieves the following results on the evaluation set: - Loss: 1.1257 - Wer: 0.4631 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-br-d2 --dataset mozilla-foundation/common_voice_8_0 --config br --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Breton language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00034 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 750 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 14.0379 | 0.68 | 100 | 5.6808 | 1.0 | | 3.9145 | 1.35 | 200 | 3.1970 | 1.0 | | 3.0293 | 2.03 | 300 | 2.9513 | 1.0 | | 2.0927 | 2.7 | 400 | 1.4545 | 0.8887 | | 1.1556 | 3.38 | 500 | 1.0966 | 0.7564 | | 0.9628 | 4.05 | 600 | 0.9808 | 0.7364 | | 0.7869 | 4.73 | 700 | 1.0488 | 0.7355 | | 0.703 | 5.41 | 800 | 0.9500 | 0.6881 | | 0.6657 | 6.08 | 900 | 0.9309 | 0.6259 | | 0.5663 | 6.76 | 1000 | 0.9133 | 0.6357 | | 0.496 | 7.43 | 1100 | 0.9890 | 0.6028 | | 0.4748 | 8.11 | 1200 | 0.9469 | 0.5894 | | 0.4135 | 8.78 | 1300 | 0.9270 | 0.6045 | | 0.3579 | 9.46 | 1400 | 0.8818 | 0.5708 | | 0.353 | 10.14 | 1500 | 0.9244 | 0.5781 | | 0.334 | 10.81 | 1600 | 0.9009 | 0.5638 | | 0.2917 | 11.49 | 1700 | 1.0132 | 0.5828 | | 0.29 | 12.16 | 1800 | 0.9696 | 0.5668 | | 0.2691 | 12.84 | 1900 | 0.9811 | 0.5455 | | 0.25 | 13.51 | 2000 | 0.9951 | 0.5624 | | 0.2467 | 14.19 | 2100 | 0.9653 | 0.5573 | | 0.2242 | 14.86 | 2200 | 0.9714 | 0.5378 | | 0.2066 | 15.54 | 2300 | 0.9829 | 0.5394 | | 0.2075 | 16.22 | 2400 | 1.0547 | 0.5520 | | 0.1923 | 16.89 | 2500 | 1.0014 | 0.5397 | | 0.1919 | 17.57 | 2600 | 0.9978 | 0.5477 | | 0.1908 | 18.24 | 2700 | 1.1064 | 0.5397 | | 0.157 | 18.92 | 2800 | 1.0629 | 0.5238 | | 0.159 | 19.59 | 2900 | 1.0642 | 0.5321 | | 0.1652 | 20.27 | 3000 | 1.0207 | 0.5328 | | 0.141 | 20.95 | 3100 | 0.9948 | 0.5312 | | 0.1417 | 21.62 | 3200 | 1.0338 | 0.5328 | | 0.1514 | 22.3 | 3300 | 1.0513 | 0.5313 | | 0.1365 | 22.97 | 3400 | 1.0357 | 0.5291 | | 0.1319 | 23.65 | 3500 | 1.0587 | 0.5167 | | 0.1298 | 24.32 | 3600 | 1.0636 | 0.5236 | | 0.1245 | 25.0 | 3700 | 1.1367 | 0.5280 | | 0.1114 | 25.68 | 3800 | 1.0633 | 0.5200 | | 0.1088 | 26.35 | 3900 | 1.0495 | 0.5210 | | 0.1175 | 27.03 | 4000 | 1.0897 | 0.5095 | | 0.1043 | 27.7 | 4100 | 1.0580 | 0.5309 | | 0.0951 | 28.38 | 4200 | 1.0448 | 0.5067 | | 0.1011 | 29.05 | 4300 | 1.0665 | 0.5137 | | 0.0889 | 29.73 | 4400 | 1.0579 | 0.5026 | | 0.0833 | 30.41 | 4500 | 1.0740 | 0.5037 | | 0.0889 | 31.08 | 4600 | 1.0933 | 0.5083 | | 0.0784 | 31.76 | 4700 | 1.0715 | 0.5089 | | 0.0767 | 32.43 | 4800 | 1.0658 | 0.5049 | | 0.0769 | 33.11 | 4900 | 1.1118 | 0.4979 | | 0.0722 | 33.78 | 5000 | 1.1413 | 0.4986 | | 0.0709 | 34.46 | 5100 | 1.0706 | 0.4885 | | 0.0664 | 35.14 | 5200 | 1.1217 | 0.4884 | | 0.0648 | 35.81 | 5300 | 1.1298 | 0.4941 | | 0.0657 | 36.49 | 5400 | 1.1330 | 0.4920 | | 0.0582 | 37.16 | 5500 | 1.0598 | 0.4835 | | 0.0602 | 37.84 | 5600 | 1.1097 | 0.4943 | | 0.0598 | 38.51 | 5700 | 1.0976 | 0.4876 | | 0.0547 | 39.19 | 5800 | 1.0734 | 0.4825 | | 0.0561 | 39.86 | 5900 | 1.0926 | 0.4850 | | 0.0516 | 40.54 | 6000 | 1.1579 | 0.4751 | | 0.0478 | 41.22 | 6100 | 1.1384 | 0.4706 | | 0.0396 | 41.89 | 6200 | 1.1462 | 0.4739 | | 0.0472 | 42.57 | 6300 | 1.1277 | 0.4732 | | 0.0447 | 43.24 | 6400 | 1.1517 | 0.4752 | | 0.0423 | 43.92 | 6500 | 1.1219 | 0.4784 | | 0.0426 | 44.59 | 6600 | 1.1311 | 0.4724 | | 0.0391 | 45.27 | 6700 | 1.1135 | 0.4692 | | 0.0362 | 45.95 | 6800 | 1.0878 | 0.4645 | | 0.0329 | 46.62 | 6900 | 1.1137 | 0.4668 | | 0.0356 | 47.3 | 7000 | 1.1233 | 0.4687 | | 0.0328 | 47.97 | 7100 | 1.1238 | 0.4653 | | 0.0323 | 48.65 | 7200 | 1.1307 | 0.4646 | | 0.0325 | 49.32 | 7300 | 1.1242 | 0.4645 | | 0.03 | 50.0 | 7400 | 1.1257 | 0.4631 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["br"], "license": "apache-2.0", "tags": ["generated_from_trainer", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-br-d2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "br"}, "metrics": [{"type": "wer", "value": 0.49770598355954887, "name": "Test WER"}, {"type": "cer", "value": 0.18090500890299605, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "br"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-br-d2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #robust-speech-event #hf-asr-leaderboard #br #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-br-d2 =============================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - BR dataset. It achieves the following results on the evaluation set: * Loss: 1.1257 * Wer: 0.4631 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-br-d2 --dataset mozilla-foundation/common\_voice\_8\_0 --config br --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Breton language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.00034 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 750 * num\_epochs: 50 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-gn-k1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - GN dataset. It achieves the following results on the evaluation set: - Loss: 0.9220 - Wer: 0.6631 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-gn-k1 --dataset mozilla-foundation/common_voice_8_0 --config gn --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data NA ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00018 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 600 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 15.9402 | 8.32 | 100 | 6.9185 | 1.0 | | 4.6367 | 16.64 | 200 | 3.7416 | 1.0 | | 3.4337 | 24.96 | 300 | 3.2581 | 1.0 | | 3.2307 | 33.32 | 400 | 2.8008 | 1.0 | | 1.3182 | 41.64 | 500 | 0.8359 | 0.8171 | | 0.409 | 49.96 | 600 | 0.8470 | 0.8323 | | 0.2573 | 58.32 | 700 | 0.7823 | 0.7576 | | 0.1969 | 66.64 | 800 | 0.8306 | 0.7424 | | 0.1469 | 74.96 | 900 | 0.9225 | 0.7713 | | 0.1172 | 83.32 | 1000 | 0.7903 | 0.6951 | | 0.1017 | 91.64 | 1100 | 0.8519 | 0.6921 | | 0.0851 | 99.96 | 1200 | 0.8129 | 0.6646 | | 0.071 | 108.32 | 1300 | 0.8614 | 0.7043 | | 0.061 | 116.64 | 1400 | 0.8414 | 0.6921 | | 0.0552 | 124.96 | 1500 | 0.8649 | 0.6905 | | 0.0465 | 133.32 | 1600 | 0.8575 | 0.6646 | | 0.0381 | 141.64 | 1700 | 0.8802 | 0.6723 | | 0.0338 | 149.96 | 1800 | 0.8731 | 0.6845 | | 0.0306 | 158.32 | 1900 | 0.9003 | 0.6585 | | 0.0236 | 166.64 | 2000 | 0.9408 | 0.6616 | | 0.021 | 174.96 | 2100 | 0.9353 | 0.6723 | | 0.0212 | 183.32 | 2200 | 0.9269 | 0.6570 | | 0.0191 | 191.64 | 2300 | 0.9277 | 0.6662 | | 0.0161 | 199.96 | 2400 | 0.9220 | 0.6631 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["gn"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "gn", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-gn-k1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "gn"}, "metrics": [{"type": "wer", "value": 0.711890243902439, "name": "Test WER"}, {"type": "cer", "value": 0.13311897106109324, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "gn"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-gn-k1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #gn #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-gn-k1 =============================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - GN dataset. It achieves the following results on the evaluation set: * Loss: 0.9220 * Wer: 0.6631 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-gn-k1 --dataset mozilla-foundation/common\_voice\_8\_0 --config gn --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data NA ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.00018 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 600 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hi-CV7 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.6588 - Wer: 0.2987 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-CV7 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data NA ### Training hyperparameters The following hyperparameters were used during training: # - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 60 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 12.809 | 1.36 | 200 | 6.2066 | 1.0 | | 4.3402 | 2.72 | 400 | 3.5184 | 1.0 | | 3.4365 | 4.08 | 600 | 3.2779 | 1.0 | | 1.8643 | 5.44 | 800 | 0.9875 | 0.6270 | | 0.7504 | 6.8 | 1000 | 0.6382 | 0.4666 | | 0.5328 | 8.16 | 1200 | 0.6075 | 0.4505 | | 0.4364 | 9.52 | 1400 | 0.5785 | 0.4215 | | 0.3777 | 10.88 | 1600 | 0.6279 | 0.4227 | | 0.3374 | 12.24 | 1800 | 0.6536 | 0.4192 | | 0.3236 | 13.6 | 2000 | 0.5911 | 0.4047 | | 0.2877 | 14.96 | 2200 | 0.5955 | 0.4097 | | 0.2643 | 16.33 | 2400 | 0.5923 | 0.3744 | | 0.2421 | 17.68 | 2600 | 0.6307 | 0.3814 | | 0.2218 | 19.05 | 2800 | 0.6036 | 0.3764 | | 0.2046 | 20.41 | 3000 | 0.6286 | 0.3797 | | 0.191 | 21.77 | 3200 | 0.6517 | 0.3889 | | 0.1856 | 23.13 | 3400 | 0.6193 | 0.3661 | | 0.1721 | 24.49 | 3600 | 0.7034 | 0.3727 | | 0.1656 | 25.85 | 3800 | 0.6293 | 0.3591 | | 0.1532 | 27.21 | 4000 | 0.6075 | 0.3611 | | 0.1507 | 28.57 | 4200 | 0.6313 | 0.3565 | | 0.1381 | 29.93 | 4400 | 0.6564 | 0.3578 | | 0.1359 | 31.29 | 4600 | 0.6724 | 0.3543 | | 0.1248 | 32.65 | 4800 | 0.6789 | 0.3512 | | 0.1198 | 34.01 | 5000 | 0.6442 | 0.3539 | | 0.1125 | 35.37 | 5200 | 0.6676 | 0.3419 | | 0.1036 | 36.73 | 5400 | 0.7017 | 0.3435 | | 0.0982 | 38.09 | 5600 | 0.6828 | 0.3319 | | 0.0971 | 39.45 | 5800 | 0.6112 | 0.3351 | | 0.0968 | 40.81 | 6000 | 0.6424 | 0.3252 | | 0.0893 | 42.18 | 6200 | 0.6707 | 0.3304 | | 0.0878 | 43.54 | 6400 | 0.6432 | 0.3236 | | 0.0827 | 44.89 | 6600 | 0.6696 | 0.3240 | | 0.0788 | 46.26 | 6800 | 0.6564 | 0.3180 | | 0.0753 | 47.62 | 7000 | 0.6574 | 0.3130 | | 0.0674 | 48.98 | 7200 | 0.6698 | 0.3175 | | 0.0676 | 50.34 | 7400 | 0.6441 | 0.3142 | | 0.0626 | 51.7 | 7600 | 0.6642 | 0.3121 | | 0.0617 | 53.06 | 7800 | 0.6615 | 0.3117 | | 0.0599 | 54.42 | 8000 | 0.6634 | 0.3059 | | 0.0538 | 55.78 | 8200 | 0.6464 | 0.3033 | | 0.0571 | 57.14 | 8400 | 0.6503 | 0.3018 | | 0.0491 | 58.5 | 8600 | 0.6625 | 0.3025 | | 0.0511 | 59.86 | 8800 | 0.6588 | 0.2987 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "hi", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hi-CV7", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "hi"}, "metrics": [{"type": "wer", "value": 35.31946325249292, "name": "Test WER"}, {"type": "cer", "value": 11.310803379493075, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "vot"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-hi-CV7

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #hi #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-hi-CV7 ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - HI dataset. It achieves the following results on the evaluation set: * Loss: 0.6588 * Wer: 0.2987 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-CV7 --dataset mozilla-foundation/common\_voice\_7\_0 --config hi --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data NA ### Training hyperparameters The following hyperparameters were used during training: * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 2000 * num\_epochs: 60 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hi-cv8-b2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.7322 - Wer: 0.3469 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2 --dataset mozilla-foundation/common_voice_8_0 --config hi --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Hindi language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00025 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 700 - num_epochs: 35 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.6226 | 1.04 | 200 | 3.8855 | 1.0 | | 3.4678 | 2.07 | 400 | 3.4283 | 1.0 | | 2.3668 | 3.11 | 600 | 1.0743 | 0.7175 | | 0.7308 | 4.15 | 800 | 0.7663 | 0.5498 | | 0.4985 | 5.18 | 1000 | 0.6957 | 0.5001 | | 0.3817 | 6.22 | 1200 | 0.6932 | 0.4866 | | 0.3281 | 7.25 | 1400 | 0.7034 | 0.4983 | | 0.2752 | 8.29 | 1600 | 0.6588 | 0.4606 | | 0.2475 | 9.33 | 1800 | 0.6514 | 0.4328 | | 0.219 | 10.36 | 2000 | 0.6396 | 0.4176 | | 0.2036 | 11.4 | 2200 | 0.6867 | 0.4162 | | 0.1793 | 12.44 | 2400 | 0.6943 | 0.4196 | | 0.1724 | 13.47 | 2600 | 0.6862 | 0.4260 | | 0.1554 | 14.51 | 2800 | 0.7615 | 0.4222 | | 0.151 | 15.54 | 3000 | 0.7058 | 0.4110 | | 0.1335 | 16.58 | 3200 | 0.7172 | 0.3986 | | 0.1326 | 17.62 | 3400 | 0.7182 | 0.3923 | | 0.1225 | 18.65 | 3600 | 0.6995 | 0.3910 | | 0.1146 | 19.69 | 3800 | 0.7075 | 0.3875 | | 0.108 | 20.73 | 4000 | 0.7297 | 0.3858 | | 0.1048 | 21.76 | 4200 | 0.7413 | 0.3850 | | 0.0979 | 22.8 | 4400 | 0.7452 | 0.3793 | | 0.0946 | 23.83 | 4600 | 0.7436 | 0.3759 | | 0.0897 | 24.87 | 4800 | 0.7289 | 0.3754 | | 0.0854 | 25.91 | 5000 | 0.7271 | 0.3667 | | 0.0803 | 26.94 | 5200 | 0.7378 | 0.3656 | | 0.0752 | 27.98 | 5400 | 0.7488 | 0.3680 | | 0.0718 | 29.02 | 5600 | 0.7185 | 0.3619 | | 0.0702 | 30.05 | 5800 | 0.7428 | 0.3554 | | 0.0653 | 31.09 | 6000 | 0.7447 | 0.3559 | | 0.0638 | 32.12 | 6200 | 0.7327 | 0.3523 | | 0.058 | 33.16 | 6400 | 0.7339 | 0.3488 | | 0.0594 | 34.2 | 6600 | 0.7322 | 0.3469 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hi-cv8-b2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_8_0", "args": "hi"}, "metrics": [{"type": "wer", "value": 0.3891350503092403, "name": "Test WER"}, {"type": "cer", "value": 0.13016327327131985, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "hi"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #hi #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-hi-cv8-b2 =================================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HI dataset. It achieves the following results on the evaluation set: * Loss: 0.7322 * Wer: 0.3469 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8-b2 --dataset mozilla-foundation/common\_voice\_8\_0 --config hi --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Hindi language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.00025 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 700 * num\_epochs: 35 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hi-cv8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.6510 - Wer: 0.3179 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset mozilla-foundation/common_voice_8_0 --config hi --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset speech-recognition-community-v2/dev_data --config hi --split validation --chunk_length_s 10 --stride_length_s 1 Note: Hindi language not found in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 12.5576 | 1.04 | 200 | 6.6594 | 1.0 | | 4.4069 | 2.07 | 400 | 3.6011 | 1.0 | | 3.4273 | 3.11 | 600 | 3.3370 | 1.0 | | 2.1108 | 4.15 | 800 | 1.0641 | 0.6562 | | 0.8817 | 5.18 | 1000 | 0.7178 | 0.5172 | | 0.6508 | 6.22 | 1200 | 0.6612 | 0.4839 | | 0.5524 | 7.25 | 1400 | 0.6458 | 0.4889 | | 0.4992 | 8.29 | 1600 | 0.5791 | 0.4382 | | 0.4669 | 9.33 | 1800 | 0.6039 | 0.4352 | | 0.4441 | 10.36 | 2000 | 0.6276 | 0.4297 | | 0.4172 | 11.4 | 2200 | 0.6183 | 0.4474 | | 0.3872 | 12.44 | 2400 | 0.5886 | 0.4231 | | 0.3692 | 13.47 | 2600 | 0.6448 | 0.4399 | | 0.3385 | 14.51 | 2800 | 0.6344 | 0.4075 | | 0.3246 | 15.54 | 3000 | 0.5896 | 0.4087 | | 0.3026 | 16.58 | 3200 | 0.6158 | 0.4016 | | 0.284 | 17.62 | 3400 | 0.6038 | 0.3906 | | 0.2682 | 18.65 | 3600 | 0.6165 | 0.3900 | | 0.2577 | 19.69 | 3800 | 0.5754 | 0.3805 | | 0.2509 | 20.73 | 4000 | 0.6028 | 0.3925 | | 0.2426 | 21.76 | 4200 | 0.6335 | 0.4138 | | 0.2346 | 22.8 | 4400 | 0.6128 | 0.3870 | | 0.2205 | 23.83 | 4600 | 0.6223 | 0.3831 | | 0.2104 | 24.87 | 4800 | 0.6122 | 0.3781 | | 0.1992 | 25.91 | 5000 | 0.6467 | 0.3792 | | 0.1916 | 26.94 | 5200 | 0.6277 | 0.3636 | | 0.1835 | 27.98 | 5400 | 0.6317 | 0.3773 | | 0.1776 | 29.02 | 5600 | 0.6124 | 0.3614 | | 0.1751 | 30.05 | 5800 | 0.6475 | 0.3628 | | 0.1662 | 31.09 | 6000 | 0.6266 | 0.3504 | | 0.1584 | 32.12 | 6200 | 0.6347 | 0.3532 | | 0.1494 | 33.16 | 6400 | 0.6636 | 0.3491 | | 0.1457 | 34.2 | 6600 | 0.6334 | 0.3507 | | 0.1427 | 35.23 | 6800 | 0.6397 | 0.3442 | | 0.1397 | 36.27 | 7000 | 0.6468 | 0.3496 | | 0.1283 | 37.31 | 7200 | 0.6291 | 0.3416 | | 0.1255 | 38.34 | 7400 | 0.6652 | 0.3461 | | 0.1195 | 39.38 | 7600 | 0.6587 | 0.3342 | | 0.1169 | 40.41 | 7800 | 0.6478 | 0.3319 | | 0.1126 | 41.45 | 8000 | 0.6280 | 0.3291 | | 0.1112 | 42.49 | 8200 | 0.6434 | 0.3290 | | 0.1069 | 43.52 | 8400 | 0.6542 | 0.3268 | | 0.1027 | 44.56 | 8600 | 0.6536 | 0.3239 | | 0.0993 | 45.6 | 8800 | 0.6622 | 0.3257 | | 0.0973 | 46.63 | 9000 | 0.6572 | 0.3192 | | 0.0911 | 47.67 | 9200 | 0.6522 | 0.3175 | | 0.0897 | 48.7 | 9400 | 0.6521 | 0.3200 | | 0.0905 | 49.74 | 9600 | 0.6510 | 0.3179 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hi", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hi-cv8", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "hi"}, "metrics": [{"type": "wer", "value": 0.3628727037755008, "name": "Test WER"}, {"type": "cer", "value": 0.11933724247521164, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "hi"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hi #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-hi-cv8 ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HI dataset. It achieves the following results on the evaluation set: * Loss: 0.6510 * Wer: 0.3179 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset mozilla-foundation/common\_voice\_8\_0 --config hi --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-cv8 --dataset speech-recognition-community-v2/dev\_data --config hi --split validation --chunk\_length\_s 10 --stride\_length\_s 1 Note: Hindi language not found in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 2000 * num\_epochs: 50 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hi-d3 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HI dataset. It achieves the following results on the evaluation set: - Loss: 0.7988 - Wer: 0.3713 ###Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Hindi language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000388 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 750 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.2826 | 1.36 | 200 | 3.5253 | 1.0 | | 2.7019 | 2.72 | 400 | 1.1744 | 0.7360 | | 0.7358 | 4.08 | 600 | 0.7781 | 0.5501 | | 0.4942 | 5.44 | 800 | 0.7590 | 0.5345 | | 0.4056 | 6.8 | 1000 | 0.6885 | 0.4776 | | 0.3243 | 8.16 | 1200 | 0.7195 | 0.4861 | | 0.2785 | 9.52 | 1400 | 0.7473 | 0.4930 | | 0.2448 | 10.88 | 1600 | 0.7201 | 0.4574 | | 0.2155 | 12.24 | 1800 | 0.7686 | 0.4648 | | 0.2039 | 13.6 | 2000 | 0.7440 | 0.4624 | | 0.1792 | 14.96 | 2200 | 0.7815 | 0.4658 | | 0.1695 | 16.33 | 2400 | 0.7678 | 0.4557 | | 0.1598 | 17.68 | 2600 | 0.7468 | 0.4393 | | 0.1568 | 19.05 | 2800 | 0.7440 | 0.4422 | | 0.1391 | 20.41 | 3000 | 0.7656 | 0.4317 | | 0.1283 | 21.77 | 3200 | 0.7892 | 0.4299 | | 0.1194 | 23.13 | 3400 | 0.7646 | 0.4192 | | 0.1116 | 24.49 | 3600 | 0.8156 | 0.4330 | | 0.1111 | 25.85 | 3800 | 0.7661 | 0.4322 | | 0.1023 | 27.21 | 4000 | 0.7419 | 0.4276 | | 0.1007 | 28.57 | 4200 | 0.8488 | 0.4245 | | 0.0925 | 29.93 | 4400 | 0.8062 | 0.4070 | | 0.0918 | 31.29 | 4600 | 0.8412 | 0.4218 | | 0.0813 | 32.65 | 4800 | 0.8045 | 0.4087 | | 0.0805 | 34.01 | 5000 | 0.8411 | 0.4113 | | 0.0774 | 35.37 | 5200 | 0.7664 | 0.3943 | | 0.0666 | 36.73 | 5400 | 0.8082 | 0.3939 | | 0.0655 | 38.09 | 5600 | 0.7948 | 0.4000 | | 0.0617 | 39.45 | 5800 | 0.8084 | 0.3932 | | 0.0606 | 40.81 | 6000 | 0.8223 | 0.3841 | | 0.0569 | 42.18 | 6200 | 0.7892 | 0.3832 | | 0.0544 | 43.54 | 6400 | 0.8326 | 0.3834 | | 0.0508 | 44.89 | 6600 | 0.7952 | 0.3774 | | 0.0492 | 46.26 | 6800 | 0.7923 | 0.3756 | | 0.0459 | 47.62 | 7000 | 0.7925 | 0.3701 | | 0.0423 | 48.98 | 7200 | 0.7988 | 0.3713 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_7_0", "generated_from_trainer", "hi", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_7_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hi-d3", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "vot"}, "metrics": [{"type": "wer", "value": 0.4204111781361566, "name": "Test WER"}, {"type": "cer", "value": 0.13869169624556316, "name": "Test CER"}, {"type": "wer", "value": 42.04, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "hi"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_7_0 #generated_from_trainer #hi #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-hi-d3 =============================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 - HI dataset. It achieves the following results on the evaluation set: * Loss: 0.7988 * Wer: 0.3713 ###Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-d3 --dataset mozilla-foundation/common\_voice\_7\_0 --config hi --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Hindi language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000388 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 750 * num\_epochs: 50 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hi-wx1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 -HI dataset. It achieves the following results on the evaluation set: - Loss: 0.6552 - Wer: 0.3200 Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data NA ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00024 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1800 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 12.2663 | 1.36 | 200 | 5.9245 | 1.0 | | 4.1856 | 2.72 | 400 | 3.4968 | 1.0 | | 3.3908 | 4.08 | 600 | 2.9970 | 1.0 | | 1.5444 | 5.44 | 800 | 0.9071 | 0.6139 | | 0.7237 | 6.8 | 1000 | 0.6508 | 0.4862 | | 0.5323 | 8.16 | 1200 | 0.6217 | 0.4647 | | 0.4426 | 9.52 | 1400 | 0.5785 | 0.4288 | | 0.3933 | 10.88 | 1600 | 0.5935 | 0.4217 | | 0.3532 | 12.24 | 1800 | 0.6358 | 0.4465 | | 0.3319 | 13.6 | 2000 | 0.5789 | 0.4118 | | 0.2877 | 14.96 | 2200 | 0.6163 | 0.4056 | | 0.2663 | 16.33 | 2400 | 0.6176 | 0.3893 | | 0.2511 | 17.68 | 2600 | 0.6065 | 0.3999 | | 0.2275 | 19.05 | 2800 | 0.6183 | 0.3842 | | 0.2098 | 20.41 | 3000 | 0.6486 | 0.3864 | | 0.1943 | 21.77 | 3200 | 0.6365 | 0.3885 | | 0.1877 | 23.13 | 3400 | 0.6013 | 0.3677 | | 0.1679 | 24.49 | 3600 | 0.6451 | 0.3795 | | 0.1667 | 25.85 | 3800 | 0.6410 | 0.3635 | | 0.1514 | 27.21 | 4000 | 0.6000 | 0.3577 | | 0.1453 | 28.57 | 4200 | 0.6020 | 0.3518 | | 0.134 | 29.93 | 4400 | 0.6531 | 0.3517 | | 0.1354 | 31.29 | 4600 | 0.6874 | 0.3578 | | 0.1224 | 32.65 | 4800 | 0.6519 | 0.3492 | | 0.1199 | 34.01 | 5000 | 0.6553 | 0.3490 | | 0.1077 | 35.37 | 5200 | 0.6621 | 0.3429 | | 0.0997 | 36.73 | 5400 | 0.6641 | 0.3413 | | 0.0964 | 38.09 | 5600 | 0.6722 | 0.3385 | | 0.0931 | 39.45 | 5800 | 0.6365 | 0.3363 | | 0.0944 | 40.81 | 6000 | 0.6454 | 0.3326 | | 0.0862 | 42.18 | 6200 | 0.6497 | 0.3256 | | 0.0848 | 43.54 | 6400 | 0.6599 | 0.3226 | | 0.0793 | 44.89 | 6600 | 0.6625 | 0.3232 | | 0.076 | 46.26 | 6800 | 0.6463 | 0.3186 | | 0.0749 | 47.62 | 7000 | 0.6559 | 0.3225 | | 0.0663 | 48.98 | 7200 | 0.6552 | 0.3200 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["hi"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_7_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hi-wx1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 7", "type": "mozilla-foundation/common_voice_7_0", "args": "hi"}, "metrics": [{"type": "wer", "value": 37.19684845500431, "name": "Test WER"}, {"type": "cer", "value": 11.763235514672798, "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #hi #dataset-mozilla-foundation/common_voice_7_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-hi-wx1 ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_7\_0 -HI dataset. It achieves the following results on the evaluation set: * Loss: 0.6552 * Wer: 0.3200 Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 --dataset mozilla-foundation/common\_voice\_7\_0 --config hi --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data NA ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.00024 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1800 * num\_epochs: 50 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hsb-v1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HSB dataset. It achieves the following results on the evaluation set: - Loss: 0.5684 - Wer: 0.4402 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v1 --dataset mozilla-foundation/common_voice_8_0 --config hsb --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Upper Sorbian language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00045 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.972 | 3.23 | 100 | 3.7498 | 1.0 | | 3.3401 | 6.45 | 200 | 3.2320 | 1.0 | | 3.2046 | 9.68 | 300 | 3.1741 | 0.9806 | | 2.4031 | 12.9 | 400 | 1.0579 | 0.8996 | | 1.0427 | 16.13 | 500 | 0.7989 | 0.7557 | | 0.741 | 19.35 | 600 | 0.6405 | 0.6299 | | 0.5699 | 22.58 | 700 | 0.6129 | 0.5928 | | 0.4607 | 25.81 | 800 | 0.6548 | 0.5695 | | 0.3827 | 29.03 | 900 | 0.6268 | 0.5190 | | 0.3282 | 32.26 | 1000 | 0.5919 | 0.5016 | | 0.2764 | 35.48 | 1100 | 0.5953 | 0.4805 | | 0.2335 | 38.71 | 1200 | 0.5717 | 0.4728 | | 0.2106 | 41.94 | 1300 | 0.5674 | 0.4569 | | 0.1859 | 45.16 | 1400 | 0.5685 | 0.4502 | | 0.1592 | 48.39 | 1500 | 0.5684 | 0.4402 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"language": ["hsb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hsb", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hsb-v1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "hsb"}, "metrics": [{"type": "wer", "value": 0.4393, "name": "Test WER"}, {"type": "cer", "value": 0.1036, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "hsb"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hsb #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-hsb-v1 ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HSB dataset. It achieves the following results on the evaluation set: * Loss: 0.5684 * Wer: 0.4402 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v1 --dataset mozilla-foundation/common\_voice\_8\_0 --config hsb --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Upper Sorbian language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.00045 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 50 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.1 * Pytorch 1.10.0+cu111 * Datasets 1.18.2 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hsb-v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HSB dataset. It achieves the following results on the evaluation set: - Loss: 0.5328 - Wer: 0.4596 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v2 --dataset mozilla-foundation/common_voice_8_0 --config hsb --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Upper Sorbian (hsb) not found in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00045 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.5979 | 3.23 | 100 | 3.5602 | 1.0 | | 3.303 | 6.45 | 200 | 3.2238 | 1.0 | | 3.2034 | 9.68 | 300 | 3.2002 | 0.9888 | | 2.7986 | 12.9 | 400 | 1.2408 | 0.9210 | | 1.3869 | 16.13 | 500 | 0.7973 | 0.7462 | | 1.0228 | 19.35 | 600 | 0.6722 | 0.6788 | | 0.8311 | 22.58 | 700 | 0.6100 | 0.6150 | | 0.717 | 25.81 | 800 | 0.6236 | 0.6013 | | 0.6264 | 29.03 | 900 | 0.6031 | 0.5575 | | 0.5494 | 32.26 | 1000 | 0.5656 | 0.5309 | | 0.4781 | 35.48 | 1100 | 0.5289 | 0.4996 | | 0.4311 | 38.71 | 1200 | 0.5375 | 0.4768 | | 0.3902 | 41.94 | 1300 | 0.5246 | 0.4703 | | 0.3508 | 45.16 | 1400 | 0.5382 | 0.4696 | | 0.3199 | 48.39 | 1500 | 0.5328 | 0.4596 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"language": ["hsb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hsb", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hsb-v2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "hsb"}, "metrics": [{"type": "wer", "value": 0.4654228855721393, "name": "Test WER"}, {"type": "cer", "value": 0.11351049990708047, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "hsb"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hsb #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-hsb-v2 ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HSB dataset. It achieves the following results on the evaluation set: * Loss: 0.5328 * Wer: 0.4596 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v2 --dataset mozilla-foundation/common\_voice\_8\_0 --config hsb --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Upper Sorbian (hsb) not found in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.00045 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 50 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.1 * Pytorch 1.10.0+cu111 * Datasets 1.18.2 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-hsb-v3 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HSB dataset. It achieves the following results on the evaluation set: - Loss: 0.6549 - Wer: 0.4827 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3 --dataset mozilla-foundation/common_voice_8_0 --config hsb --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Upper Sorbian (hsb) language not found in speech-recognition-community-v2/dev_data! ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.00045 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.8951 | 3.23 | 100 | 3.6396 | 1.0 | | 3.314 | 6.45 | 200 | 3.2331 | 1.0 | | 3.1931 | 9.68 | 300 | 3.0947 | 0.9906 | | 1.7079 | 12.9 | 400 | 0.8865 | 0.8499 | | 0.6859 | 16.13 | 500 | 0.7994 | 0.7529 | | 0.4804 | 19.35 | 600 | 0.7783 | 0.7069 | | 0.3506 | 22.58 | 700 | 0.6904 | 0.6321 | | 0.2695 | 25.81 | 800 | 0.6519 | 0.5926 | | 0.222 | 29.03 | 900 | 0.7041 | 0.5720 | | 0.1828 | 32.26 | 1000 | 0.6608 | 0.5513 | | 0.1474 | 35.48 | 1100 | 0.7129 | 0.5319 | | 0.1269 | 38.71 | 1200 | 0.6664 | 0.5056 | | 0.1077 | 41.94 | 1300 | 0.6712 | 0.4942 | | 0.0934 | 45.16 | 1400 | 0.6467 | 0.4879 | | 0.0819 | 48.39 | 1500 | 0.6549 | 0.4827 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"language": ["hsb"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "hsb", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-hsb-v3", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "hsb"}, "metrics": [{"type": "wer", "value": 0.4763681592039801, "name": "Test WER"}, {"type": "cer", "value": 0.11194945177476305, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "hsb"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #hsb #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-hsb-v3 ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - HSB dataset. It achieves the following results on the evaluation set: * Loss: 0.6549 * Wer: 0.4827 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-hsb-v3 --dataset mozilla-foundation/common\_voice\_8\_0 --config hsb --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Upper Sorbian (hsb) language not found in speech-recognition-community-v2/dev\_data! ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.00045 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 50 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.1 * Pytorch 1.10.0+cu111 * Datasets 1.18.2 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - KK dataset. It achieves the following results on the evaluation set: - Loss: 0.7149 - Wer: 0.451 # Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-kk-with-LM --dataset mozilla-foundation/common_voice_8_0 --config kk --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Kazakh language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000222 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 150.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 9.6799 | 9.09 | 200 | 3.6119 | 1.0 | | 3.1332 | 18.18 | 400 | 2.5352 | 1.005 | | 1.0465 | 27.27 | 600 | 0.6169 | 0.682 | | 0.3452 | 36.36 | 800 | 0.6572 | 0.607 | | 0.2575 | 45.44 | 1000 | 0.6527 | 0.578 | | 0.2088 | 54.53 | 1200 | 0.6828 | 0.551 | | 0.158 | 63.62 | 1400 | 0.7074 | 0.5575 | | 0.1309 | 72.71 | 1600 | 0.6523 | 0.5595 | | 0.1074 | 81.8 | 1800 | 0.7262 | 0.5415 | | 0.087 | 90.89 | 2000 | 0.7199 | 0.521 | | 0.0711 | 99.98 | 2200 | 0.7113 | 0.523 | | 0.0601 | 109.09 | 2400 | 0.6863 | 0.496 | | 0.0451 | 118.18 | 2600 | 0.6998 | 0.483 | | 0.0378 | 127.27 | 2800 | 0.6971 | 0.4615 | | 0.0319 | 136.36 | 3000 | 0.7119 | 0.4475 | | 0.0305 | 145.44 | 3200 | 0.7181 | 0.459 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0 ### Evaluation Command !python eval.py \ --model_id DrishtiSharma/wav2vec2-xls-r-300m-kk-n2 \ --dataset mozilla-foundation/common_voice_8_0 --config kk --split test --log_outputs

{"language": ["kk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "kk", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-kk-with-LM", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "ru"}, "metrics": [{"type": "wer", "value": 0.4355, "name": "Test WER"}, {"type": "cer", "value": 0.10469915859660263, "name": "Test CER"}, {"type": "wer", "value": 0.417, "name": "Test WER (+LM)"}, {"type": "cer", "value": 0.10319098269566598, "name": "Test CER (+LM)"}, {"type": "wer", "value": 41.7, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "kk"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "kk"}, "metrics": [{"type": "wer", "value": 67.09, "name": "Test WER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-kk-with-LM

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #kk #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - KK dataset. It achieves the following results on the evaluation set: * Loss: 0.7149 * Wer: 0.451 Evaluation Commands =================== 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-kk-with-LM --dataset mozilla-foundation/common\_voice\_8\_0 --config kk --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Kazakh language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000222 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 150.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0 ### Evaluation Command !python URL --model\_id DrishtiSharma/wav2vec2-xls-r-300m-kk-n2 --dataset mozilla-foundation/common\_voice\_8\_0 --config kk --split test --log\_outputs

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-maltese This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MT dataset. It achieves the following results on the evaluation set: - Loss: 0.2994 - Wer: 0.2781 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1800 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.0174 | 9.01 | 1000 | 3.0552 | 1.0 | | 1.0446 | 18.02 | 2000 | 0.6708 | 0.7577 | | 0.7995 | 27.03 | 3000 | 0.4202 | 0.4770 | | 0.6978 | 36.04 | 4000 | 0.3054 | 0.3494 | | 0.6189 | 45.05 | 5000 | 0.2878 | 0.3154 | | 0.5667 | 54.05 | 6000 | 0.3114 | 0.3286 | | 0.5173 | 63.06 | 7000 | 0.3085 | 0.3021 | | 0.4682 | 72.07 | 8000 | 0.3058 | 0.2969 | | 0.451 | 81.08 | 9000 | 0.3146 | 0.2907 | | 0.4213 | 90.09 | 10000 | 0.3030 | 0.2881 | | 0.4005 | 99.1 | 11000 | 0.3001 | 0.2789 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0 ### Evaluation Script !python eval.py \ --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-maltese \ --dataset mozilla-foundation/common_voice_8_0 --config mt --split test --log_outputs

{"language": ["mt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "mt", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"]}

DrishtiSharma/wav2vec2-large-xls-r-300m-maltese

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #mt #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-maltese ================================= This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - MT dataset. It achieves the following results on the evaluation set: * Loss: 0.2994 * Wer: 0.2781 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7e-05 * train\_batch\_size: 32 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1800 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0 ### Evaluation Script !python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-maltese --dataset mozilla-foundation/common\_voice\_8\_0 --config mt --split test --log\_outputs

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-mr-v2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MR dataset. It achieves the following results on the evaluation set: - Loss: 0.8729 - Wer: 0.4942 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset mozilla-foundation/common_voice_8_0 --config mr --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset speech-recognition-community-v2/dev_data --config mr --split validation --chunk_length_s 10 --stride_length_s 1 Note: Marathi language not found in speech-recognition-community-v2/dev_data! ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000333 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 8.4934 | 9.09 | 200 | 3.7326 | 1.0 | | 3.4234 | 18.18 | 400 | 3.3383 | 0.9996 | | 3.2628 | 27.27 | 600 | 2.7482 | 0.9992 | | 1.7743 | 36.36 | 800 | 0.6755 | 0.6787 | | 1.0346 | 45.45 | 1000 | 0.6067 | 0.6193 | | 0.8137 | 54.55 | 1200 | 0.6228 | 0.5612 | | 0.6637 | 63.64 | 1400 | 0.5976 | 0.5495 | | 0.5563 | 72.73 | 1600 | 0.7009 | 0.5383 | | 0.4844 | 81.82 | 1800 | 0.6662 | 0.5287 | | 0.4057 | 90.91 | 2000 | 0.6911 | 0.5303 | | 0.3582 | 100.0 | 2200 | 0.7207 | 0.5327 | | 0.3163 | 109.09 | 2400 | 0.7107 | 0.5118 | | 0.2761 | 118.18 | 2600 | 0.7538 | 0.5118 | | 0.2415 | 127.27 | 2800 | 0.7850 | 0.5178 | | 0.2127 | 136.36 | 3000 | 0.8016 | 0.5034 | | 0.1873 | 145.45 | 3200 | 0.8302 | 0.5187 | | 0.1723 | 154.55 | 3400 | 0.9085 | 0.5223 | | 0.1498 | 163.64 | 3600 | 0.8396 | 0.5126 | | 0.1425 | 172.73 | 3800 | 0.8776 | 0.5094 | | 0.1258 | 181.82 | 4000 | 0.8651 | 0.5014 | | 0.117 | 190.91 | 4200 | 0.8772 | 0.4970 | | 0.1093 | 200.0 | 4400 | 0.8729 | 0.4942 | ### Framework versions - Transformers 4.16.1 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"language": ["mr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "mr", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-mr-v2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "mr"}, "metrics": [{"type": "wer", "value": 0.49378259125551544, "name": "Test WER"}, {"type": "cer", "value": 0.12470799640610962, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "mr"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #mr #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-mr-v2 =============================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - MR dataset. It achieves the following results on the evaluation set: * Loss: 0.8729 * Wer: 0.4942 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset mozilla-foundation/common\_voice\_8\_0 --config mr --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-mr-v2 --dataset speech-recognition-community-v2/dev\_data --config mr --split validation --chunk\_length\_s 10 --stride\_length\_s 1 Note: Marathi language not found in speech-recognition-community-v2/dev\_data! ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000333 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.1 * Pytorch 1.10.0+cu111 * Datasets 1.18.2 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-myv-v1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MYV dataset. It achieves the following results on the evaluation set: - Loss: 0.8537 - Wer: 0.6160 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-myv-v1 --dataset mozilla-foundation/common_voice_8_0 --config myv --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Erzya language not found in speech-recognition-community-v2/dev_data! ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000222 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 150 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 19.453 | 1.92 | 50 | 16.4001 | 1.0 | | 9.6875 | 3.85 | 100 | 5.4468 | 1.0 | | 4.9988 | 5.77 | 150 | 4.3507 | 1.0 | | 4.1148 | 7.69 | 200 | 3.6753 | 1.0 | | 3.4922 | 9.62 | 250 | 3.3103 | 1.0 | | 3.2443 | 11.54 | 300 | 3.1741 | 1.0 | | 3.164 | 13.46 | 350 | 3.1346 | 1.0 | | 3.0954 | 15.38 | 400 | 3.0428 | 1.0 | | 3.0076 | 17.31 | 450 | 2.9137 | 1.0 | | 2.6883 | 19.23 | 500 | 2.1476 | 0.9978 | | 1.5124 | 21.15 | 550 | 0.8955 | 0.8225 | | 0.8711 | 23.08 | 600 | 0.6948 | 0.7591 | | 0.6695 | 25.0 | 650 | 0.6683 | 0.7636 | | 0.5606 | 26.92 | 700 | 0.6821 | 0.7435 | | 0.503 | 28.85 | 750 | 0.7220 | 0.7516 | | 0.4528 | 30.77 | 800 | 0.6638 | 0.7324 | | 0.4219 | 32.69 | 850 | 0.7120 | 0.7435 | | 0.4109 | 34.62 | 900 | 0.7122 | 0.7511 | | 0.3887 | 36.54 | 950 | 0.7179 | 0.7199 | | 0.3895 | 38.46 | 1000 | 0.7322 | 0.7525 | | 0.391 | 40.38 | 1050 | 0.6850 | 0.7364 | | 0.3537 | 42.31 | 1100 | 0.7571 | 0.7279 | | 0.3267 | 44.23 | 1150 | 0.7575 | 0.7257 | | 0.3195 | 46.15 | 1200 | 0.7580 | 0.6998 | | 0.2891 | 48.08 | 1250 | 0.7452 | 0.7101 | | 0.294 | 50.0 | 1300 | 0.7316 | 0.6945 | | 0.2854 | 51.92 | 1350 | 0.7241 | 0.6757 | | 0.2801 | 53.85 | 1400 | 0.7532 | 0.6887 | | 0.2502 | 55.77 | 1450 | 0.7587 | 0.6811 | | 0.2427 | 57.69 | 1500 | 0.7231 | 0.6851 | | 0.2311 | 59.62 | 1550 | 0.7288 | 0.6632 | | 0.2176 | 61.54 | 1600 | 0.7711 | 0.6664 | | 0.2117 | 63.46 | 1650 | 0.7914 | 0.6940 | | 0.2114 | 65.38 | 1700 | 0.8065 | 0.6918 | | 0.1913 | 67.31 | 1750 | 0.8372 | 0.6945 | | 0.1897 | 69.23 | 1800 | 0.8051 | 0.6869 | | 0.1865 | 71.15 | 1850 | 0.8076 | 0.6740 | | 0.1844 | 73.08 | 1900 | 0.7935 | 0.6708 | | 0.1757 | 75.0 | 1950 | 0.8015 | 0.6610 | | 0.1636 | 76.92 | 2000 | 0.7614 | 0.6414 | | 0.1637 | 78.85 | 2050 | 0.8123 | 0.6592 | | 0.1599 | 80.77 | 2100 | 0.7907 | 0.6566 | | 0.1498 | 82.69 | 2150 | 0.8641 | 0.6757 | | 0.1545 | 84.62 | 2200 | 0.7438 | 0.6682 | | 0.1433 | 86.54 | 2250 | 0.8014 | 0.6624 | | 0.1427 | 88.46 | 2300 | 0.7758 | 0.6646 | | 0.1423 | 90.38 | 2350 | 0.7741 | 0.6423 | | 0.1298 | 92.31 | 2400 | 0.7938 | 0.6414 | | 0.1111 | 94.23 | 2450 | 0.7976 | 0.6467 | | 0.1243 | 96.15 | 2500 | 0.7916 | 0.6481 | | 0.1215 | 98.08 | 2550 | 0.7594 | 0.6392 | | 0.113 | 100.0 | 2600 | 0.8236 | 0.6392 | | 0.1077 | 101.92 | 2650 | 0.7959 | 0.6347 | | 0.0988 | 103.85 | 2700 | 0.8189 | 0.6392 | | 0.0953 | 105.77 | 2750 | 0.8157 | 0.6414 | | 0.0889 | 107.69 | 2800 | 0.7946 | 0.6369 | | 0.0929 | 109.62 | 2850 | 0.8255 | 0.6360 | | 0.0822 | 111.54 | 2900 | 0.8320 | 0.6334 | | 0.086 | 113.46 | 2950 | 0.8539 | 0.6490 | | 0.0825 | 115.38 | 3000 | 0.8438 | 0.6418 | | 0.0727 | 117.31 | 3050 | 0.8568 | 0.6481 | | 0.0717 | 119.23 | 3100 | 0.8447 | 0.6512 | | 0.0815 | 121.15 | 3150 | 0.8470 | 0.6445 | | 0.0689 | 123.08 | 3200 | 0.8264 | 0.6249 | | 0.0726 | 125.0 | 3250 | 0.7981 | 0.6169 | | 0.0648 | 126.92 | 3300 | 0.8237 | 0.6200 | | 0.0632 | 128.85 | 3350 | 0.8416 | 0.6249 | | 0.06 | 130.77 | 3400 | 0.8276 | 0.6173 | | 0.0616 | 132.69 | 3450 | 0.8429 | 0.6209 | | 0.0614 | 134.62 | 3500 | 0.8485 | 0.6271 | | 0.0539 | 136.54 | 3550 | 0.8598 | 0.6218 | | 0.0555 | 138.46 | 3600 | 0.8557 | 0.6169 | | 0.0604 | 140.38 | 3650 | 0.8436 | 0.6186 | | 0.0556 | 142.31 | 3700 | 0.8428 | 0.6178 | | 0.051 | 144.23 | 3750 | 0.8440 | 0.6142 | | 0.0526 | 146.15 | 3800 | 0.8566 | 0.6142 | | 0.052 | 148.08 | 3850 | 0.8544 | 0.6178 | | 0.0519 | 150.0 | 3900 | 0.8537 | 0.6160 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"language": ["myv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "myv", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-myv-v1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "myv"}, "metrics": [{"type": "wer", "value": 0.599548532731377, "name": "Test WER"}, {"type": "cer", "value": 0.12953851902597, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "myv"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-myv-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #myv #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #has_space #region-us

wav2vec2-large-xls-r-300m-myv-v1 ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - MYV dataset. It achieves the following results on the evaluation set: * Loss: 0.8537 * Wer: 0.6160 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-myv-v1 --dataset mozilla-foundation/common\_voice\_8\_0 --config myv --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Erzya language not found in speech-recognition-community-v2/dev\_data! ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000222 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 150 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.2 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-or-d5 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - OR dataset. It achieves the following results on the evaluation set: - Loss: 0.9571 - Wer: 0.5450 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5 --dataset mozilla-foundation/common_voice_8_0 --config or --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5 --dataset speech-recognition-community-v2/dev_data --config or --split validation --chunk_length_s 10 --stride_length_s 1 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000111 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.2958 | 12.5 | 300 | 4.9014 | 1.0 | | 3.4065 | 25.0 | 600 | 3.5150 | 1.0 | | 1.5402 | 37.5 | 900 | 0.8356 | 0.7249 | | 0.6049 | 50.0 | 1200 | 0.7754 | 0.6349 | | 0.4074 | 62.5 | 1500 | 0.7994 | 0.6217 | | 0.3097 | 75.0 | 1800 | 0.8815 | 0.5985 | | 0.2593 | 87.5 | 2100 | 0.8532 | 0.5754 | | 0.2097 | 100.0 | 2400 | 0.9077 | 0.5648 | | 0.1784 | 112.5 | 2700 | 0.9047 | 0.5668 | | 0.1567 | 125.0 | 3000 | 0.9019 | 0.5728 | | 0.1315 | 137.5 | 3300 | 0.9295 | 0.5827 | | 0.1125 | 150.0 | 3600 | 0.9256 | 0.5681 | | 0.1035 | 162.5 | 3900 | 0.9148 | 0.5496 | | 0.0901 | 175.0 | 4200 | 0.9480 | 0.5483 | | 0.0817 | 187.5 | 4500 | 0.9799 | 0.5516 | | 0.079 | 200.0 | 4800 | 0.9571 | 0.5450 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["or"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "or", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-or-d5", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "or"}, "metrics": [{"type": "wer", "value": 0.579136690647482, "name": "Test WER"}, {"type": "cer", "value": 0.1572148018392818, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "or"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #or #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-or-d5 =============================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - OR dataset. It achieves the following results on the evaluation set: * Loss: 0.9571 * Wer: 0.5450 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5 --dataset mozilla-foundation/common\_voice\_8\_0 --config or --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-or-d5 --dataset speech-recognition-community-v2/dev\_data --config or --split validation --chunk\_length\_s 10 --stride\_length\_s 1 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000111 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 800 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-or-dx12 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 1.4638 - Wer: 0.5602 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-or-dx12 --dataset mozilla-foundation/common_voice_8_0 --config or --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Oriya language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 13.5059 | 4.17 | 100 | 10.3789 | 1.0 | | 4.5964 | 8.33 | 200 | 4.3294 | 1.0 | | 3.4448 | 12.5 | 300 | 3.7903 | 1.0 | | 3.3683 | 16.67 | 400 | 3.5289 | 1.0 | | 2.042 | 20.83 | 500 | 1.1531 | 0.7857 | | 0.5721 | 25.0 | 600 | 1.0267 | 0.7646 | | 0.3274 | 29.17 | 700 | 1.0773 | 0.6938 | | 0.2466 | 33.33 | 800 | 1.0323 | 0.6647 | | 0.2047 | 37.5 | 900 | 1.1255 | 0.6733 | | 0.1847 | 41.67 | 1000 | 1.1194 | 0.6515 | | 0.1453 | 45.83 | 1100 | 1.1215 | 0.6601 | | 0.1367 | 50.0 | 1200 | 1.1898 | 0.6627 | | 0.1334 | 54.17 | 1300 | 1.3082 | 0.6687 | | 0.1041 | 58.33 | 1400 | 1.2514 | 0.6177 | | 0.1024 | 62.5 | 1500 | 1.2055 | 0.6528 | | 0.0919 | 66.67 | 1600 | 1.4125 | 0.6369 | | 0.074 | 70.83 | 1700 | 1.4006 | 0.6634 | | 0.0681 | 75.0 | 1800 | 1.3943 | 0.6131 | | 0.0709 | 79.17 | 1900 | 1.3545 | 0.6296 | | 0.064 | 83.33 | 2000 | 1.2437 | 0.6237 | | 0.0552 | 87.5 | 2100 | 1.3762 | 0.6190 | | 0.056 | 91.67 | 2200 | 1.3763 | 0.6323 | | 0.0514 | 95.83 | 2300 | 1.2897 | 0.6164 | | 0.0409 | 100.0 | 2400 | 1.4257 | 0.6104 | | 0.0379 | 104.17 | 2500 | 1.4219 | 0.5853 | | 0.0367 | 108.33 | 2600 | 1.4361 | 0.6032 | | 0.0412 | 112.5 | 2700 | 1.4713 | 0.6098 | | 0.0353 | 116.67 | 2800 | 1.4132 | 0.6369 | | 0.0336 | 120.83 | 2900 | 1.5210 | 0.6098 | | 0.0302 | 125.0 | 3000 | 1.4686 | 0.5939 | | 0.0398 | 129.17 | 3100 | 1.5456 | 0.6204 | | 0.0291 | 133.33 | 3200 | 1.4111 | 0.5827 | | 0.0247 | 137.5 | 3300 | 1.3866 | 0.6151 | | 0.0196 | 141.67 | 3400 | 1.4513 | 0.5880 | | 0.0218 | 145.83 | 3500 | 1.5100 | 0.5899 | | 0.0196 | 150.0 | 3600 | 1.4936 | 0.5999 | | 0.0164 | 154.17 | 3700 | 1.5012 | 0.5701 | | 0.0168 | 158.33 | 3800 | 1.5601 | 0.5919 | | 0.0151 | 162.5 | 3900 | 1.4891 | 0.5761 | | 0.0137 | 166.67 | 4000 | 1.4839 | 0.5800 | | 0.0143 | 170.83 | 4100 | 1.4826 | 0.5754 | | 0.0114 | 175.0 | 4200 | 1.4950 | 0.5708 | | 0.0092 | 179.17 | 4300 | 1.5008 | 0.5694 | | 0.0104 | 183.33 | 4400 | 1.4774 | 0.5728 | | 0.0096 | 187.5 | 4500 | 1.4948 | 0.5767 | | 0.0105 | 191.67 | 4600 | 1.4557 | 0.5694 | | 0.009 | 195.83 | 4700 | 1.4615 | 0.5628 | | 0.0081 | 200.0 | 4800 | 1.4638 | 0.5602 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["or"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "or", "robust-speech-event"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-or-dx12", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "or"}, "metrics": [{"type": "wer", "value": 0.5947242206235012, "name": "Test WER"}, {"type": "cer", "value": 0.18272388876724327, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "or"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-or-dx12

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #or #robust-speech-event #dataset-common_voice #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-or-dx12 ================================= This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice dataset. It achieves the following results on the evaluation set: * Loss: 1.4638 * Wer: 0.5602 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-or-dx12 --dataset mozilla-foundation/common\_voice\_8\_0 --config or --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Oriya language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0004 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset. It achieves the following results on the evaluation set: - Loss: 1.0855 - Wer: 0.4755 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-pa-IN-dx1 --dataset mozilla-foundation/common_voice_8_0 --config pa-IN --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Punjabi language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1200 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.4607 | 9.26 | 500 | 2.7746 | 1.0416 | | 0.3442 | 18.52 | 1000 | 0.9114 | 0.5911 | | 0.2213 | 27.78 | 1500 | 0.9687 | 0.5751 | | 0.1242 | 37.04 | 2000 | 1.0204 | 0.5461 | | 0.0998 | 46.3 | 2500 | 1.0250 | 0.5233 | | 0.0727 | 55.56 | 3000 | 1.1072 | 0.5382 | | 0.0605 | 64.81 | 3500 | 1.0588 | 0.5073 | | 0.0458 | 74.07 | 4000 | 1.0818 | 0.5069 | | 0.0338 | 83.33 | 4500 | 1.0948 | 0.5108 | | 0.0223 | 92.59 | 5000 | 1.0986 | 0.4775 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["pa-IN"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "pa-IN", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-pa-IN-dx1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "pa-IN"}, "metrics": [{"type": "wer", "value": 0.48725989807918463, "name": "Test WER"}, {"type": "cer", "value": 0.1687305197540224, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "pa-IN"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-pa-IN-dx1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #pa-IN #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PA-IN dataset. It achieves the following results on the evaluation set: * Loss: 1.0855 * Wer: 0.4755 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-pa-IN-dx1 --dataset mozilla-foundation/common\_voice\_8\_0 --config pa-IN --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Punjabi language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0003 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1200 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-sat-a3 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SAT dataset. It achieves the following results on the evaluation set: - Loss: 0.8961 - Wer: 0.3976 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sat-a3 --dataset mozilla-foundation/common_voice_8_0 --config sat --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Note: Santali (Ol Chiki) language not found in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 11.1266 | 33.29 | 100 | 2.8577 | 1.0 | | 2.1549 | 66.57 | 200 | 1.0799 | 0.5542 | | 0.5628 | 99.86 | 300 | 0.7973 | 0.4016 | | 0.0779 | 133.29 | 400 | 0.8424 | 0.4177 | | 0.0404 | 166.57 | 500 | 0.9048 | 0.4137 | | 0.0212 | 199.86 | 600 | 0.8961 | 0.3976 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["sat"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "sat", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-sat-a3", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sat"}, "metrics": [{"type": "wer", "value": 0.357429718875502, "name": "Test WER"}, {"type": "cer", "value": 0.14203730272596843, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "sat"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-sat-a3

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #sat #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-sat-a3 ================================ This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SAT dataset. It achieves the following results on the evaluation set: * Loss: 0.8961 * Wer: 0.3976 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-sat-a3 --dataset mozilla-foundation/common\_voice\_8\_0 --config sat --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Note: Santali (Ol Chiki) language not found in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0004 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 200 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-sat-final This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SAT dataset. It achieves the following results on the evaluation set: - Loss: 0.8012 - Wer: 0.3815 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sat-final --dataset mozilla-foundation/common_voice_8_0 --config sat --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sat-final --dataset speech-recognition-community-v2/dev_data --config sat --split validation --chunk_length_s 10 --stride_length_s 1 **Note: Santali (Ol Chiki) language not found in speech-recognition-community-v2/dev_data** ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 170 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 10.6317 | 33.29 | 100 | 2.8629 | 1.0 | | 2.047 | 66.57 | 200 | 0.9516 | 0.5703 | | 0.4475 | 99.86 | 300 | 0.8539 | 0.3896 | | 0.0716 | 133.29 | 400 | 0.8277 | 0.3454 | | 0.047 | 166.57 | 500 | 0.7597 | 0.3655 | | 0.0249 | 199.86 | 600 | 0.8012 | 0.3815 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["sat"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "sat", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-sat-final", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sat"}, "metrics": [{"type": "wer", "value": 0.3493975903614458, "name": "Test WER"}, {"type": "cer", "value": 0.13773314203730272, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "sat"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-sat-final

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #sat #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-sat-final =================================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SAT dataset. It achieves the following results on the evaluation set: * Loss: 0.8012 * Wer: 0.3815 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-sat-final --dataset mozilla-foundation/common\_voice\_8\_0 --config sat --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-sat-final --dataset speech-recognition-community-v2/dev\_data --config sat --split validation --chunk\_length\_s 10 --stride\_length\_s 1 Note: Santali (Ol Chiki) language not found in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0004 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 170 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset. It achieves the following results on the evaluation set: - Loss: 0.2756 - Wer: 0.2279 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3881 | 6.1 | 500 | 2.9710 | 1.0 | | 2.6401 | 12.2 | 1000 | 1.7677 | 0.9734 | | 1.5152 | 18.29 | 1500 | 0.5564 | 0.6011 | | 1.2191 | 24.39 | 2000 | 0.4319 | 0.4390 | | 1.0237 | 30.49 | 2500 | 0.3141 | 0.3175 | | 0.8892 | 36.59 | 3000 | 0.2748 | 0.2689 | | 0.8296 | 42.68 | 3500 | 0.2680 | 0.2534 | | 0.7602 | 48.78 | 4000 | 0.2820 | 0.2506 | | 0.7186 | 54.88 | 4500 | 0.2672 | 0.2398 | | 0.6887 | 60.98 | 5000 | 0.2729 | 0.2402 | | 0.6507 | 67.07 | 5500 | 0.2767 | 0.2361 | | 0.6226 | 73.17 | 6000 | 0.2817 | 0.2332 | | 0.6024 | 79.27 | 6500 | 0.2679 | 0.2279 | | 0.5787 | 85.37 | 7000 | 0.2837 | 0.2316 | | 0.5744 | 91.46 | 7500 | 0.2838 | 0.2284 | | 0.5556 | 97.56 | 8000 | 0.2763 | 0.2281 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["sl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "sl"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-sl-with-LM-v1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sl"}, "metrics": [{"type": "wer", "value": 0.20626555409164105, "name": "Test WER"}, {"type": "cer", "value": 0.051648321634392154, "name": "Test CER"}, {"type": "wer", "value": 0.13482652613087395, "name": "Test WER (+LM)"}, {"type": "cer", "value": 0.038838663862562475, "name": "Test CER (+LM)"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "sl"}, "metrics": [{"type": "wer", "value": 0.5406156320830592, "name": "Dev WER"}, {"type": "cer", "value": 0.22249723590310583, "name": "Dev CER"}, {"type": "wer", "value": 0.49783147459727384, "name": "Dev WER (+LM)"}, {"type": "cer", "value": 0.1591062599627158, "name": "Dev CER (+LM)"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "sl"}, "metrics": [{"type": "wer", "value": 46.17, "name": "Test WER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #sl #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SL dataset. It achieves the following results on the evaluation set: * Loss: 0.2756 * Wer: 0.2279 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 --dataset mozilla-foundation/common\_voice\_8\_0 --config sl --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 --dataset speech-recognition-community-v2/dev\_data --config sl --split validation --chunk\_length\_s 10 --stride\_length\_s 1 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7.1e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset. It achieves the following results on the evaluation set: - Loss: 0.2855 - Wer: 0.2401 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.9294 | 6.1 | 500 | 2.9712 | 1.0 | | 2.8305 | 12.2 | 1000 | 1.7073 | 0.9479 | | 1.4795 | 18.29 | 1500 | 0.5756 | 0.6397 | | 1.3433 | 24.39 | 2000 | 0.4968 | 0.5424 | | 1.1766 | 30.49 | 2500 | 0.4185 | 0.4743 | | 1.0017 | 36.59 | 3000 | 0.3303 | 0.3578 | | 0.9358 | 42.68 | 3500 | 0.3003 | 0.3051 | | 0.8358 | 48.78 | 4000 | 0.3045 | 0.2884 | | 0.7647 | 54.88 | 4500 | 0.2866 | 0.2677 | | 0.7482 | 60.98 | 5000 | 0.2829 | 0.2585 | | 0.6943 | 67.07 | 5500 | 0.2782 | 0.2478 | | 0.6586 | 73.17 | 6000 | 0.2911 | 0.2537 | | 0.6425 | 79.27 | 6500 | 0.2817 | 0.2462 | | 0.6067 | 85.37 | 7000 | 0.2910 | 0.2436 | | 0.5974 | 91.46 | 7500 | 0.2875 | 0.2430 | | 0.5812 | 97.56 | 8000 | 0.2852 | 0.2396 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["sl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "sl"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-sl-with-LM-v2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sl"}, "metrics": [{"type": "wer", "value": 0.21695212999560826, "name": "Test WER"}, {"type": "cer", "value": 0.052850080572474256, "name": "Test CER"}, {"type": "wer", "value": 0.14551310203484116, "name": "Test WER (+LM)"}, {"type": "cer", "value": 0.03927566711277415, "name": "Test CER (+LM)"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "sl"}, "metrics": [{"type": "wer", "value": 0.560722380639029, "name": "Dev WER"}, {"type": "cer", "value": 0.2279626093074681, "name": "Dev CER"}, {"type": "wer", "value": 0.46486802661402354, "name": "Dev WER (+LM)"}, {"type": "cer", "value": 0.21105136194592422, "name": "Dev CER (+LM)"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "sl"}, "metrics": [{"type": "wer", "value": 46.69, "name": "Test WER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #sl #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SL dataset. It achieves the following results on the evaluation set: * Loss: 0.2855 * Wer: 0.2401 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset mozilla-foundation/common\_voice\_8\_0 --config sl --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v2 --dataset speech-recognition-community-v2/dev\_data --config sl --split validation --chunk\_length\_s 10 --stride\_length\_s 1 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-sr-v4 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SR dataset. It achieves the following results on the evaluation set: - Loss: 0.5570 - Wer: 0.3038 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4 --dataset mozilla-foundation/common_voice_8_0 --config sr --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4 --dataset speech-recognition-community-v2/dev_data --config sr --split validation --chunk_length_s 10 --stride_length_s 1 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 8.2934 | 7.5 | 300 | 2.9777 | 0.9995 | | 1.5049 | 15.0 | 600 | 0.5036 | 0.4806 | | 0.3263 | 22.5 | 900 | 0.5822 | 0.4055 | | 0.2008 | 30.0 | 1200 | 0.5609 | 0.4032 | | 0.1543 | 37.5 | 1500 | 0.5203 | 0.3710 | | 0.1158 | 45.0 | 1800 | 0.6458 | 0.3985 | | 0.0997 | 52.5 | 2100 | 0.6227 | 0.4013 | | 0.0834 | 60.0 | 2400 | 0.6048 | 0.3836 | | 0.0665 | 67.5 | 2700 | 0.6197 | 0.3686 | | 0.0602 | 75.0 | 3000 | 0.5418 | 0.3453 | | 0.0524 | 82.5 | 3300 | 0.5310 | 0.3486 | | 0.0445 | 90.0 | 3600 | 0.5599 | 0.3374 | | 0.0406 | 97.5 | 3900 | 0.5958 | 0.3327 | | 0.0358 | 105.0 | 4200 | 0.6017 | 0.3262 | | 0.0302 | 112.5 | 4500 | 0.5613 | 0.3248 | | 0.0285 | 120.0 | 4800 | 0.5659 | 0.3462 | | 0.0213 | 127.5 | 5100 | 0.5568 | 0.3206 | | 0.0215 | 135.0 | 5400 | 0.6524 | 0.3472 | | 0.0162 | 142.5 | 5700 | 0.6223 | 0.3458 | | 0.0137 | 150.0 | 6000 | 0.6625 | 0.3313 | | 0.0114 | 157.5 | 6300 | 0.5739 | 0.3336 | | 0.0101 | 165.0 | 6600 | 0.5906 | 0.3285 | | 0.008 | 172.5 | 6900 | 0.5982 | 0.3112 | | 0.0076 | 180.0 | 7200 | 0.5399 | 0.3094 | | 0.0071 | 187.5 | 7500 | 0.5387 | 0.2991 | | 0.0057 | 195.0 | 7800 | 0.5570 | 0.3038 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.2 - Tokenizers 0.11.0

{"language": ["sr"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "sr"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-sr-v4", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sr"}, "metrics": [{"type": "wer", "value": 0.303313, "name": "Test WER"}, {"type": "cer", "value": 0.1048951, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "sr"}, "metrics": [{"type": "wer", "value": 0.9486784706184245, "name": "Test WER"}, {"type": "cer", "value": 0.8084369606584945, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "sr"}, "metrics": [{"type": "wer", "value": 94.53, "name": "Test WER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #sr #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-sr-v4 =============================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SR dataset. It achieves the following results on the evaluation set: * Loss: 0.5570 * Wer: 0.3038 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4 --dataset mozilla-foundation/common\_voice\_8\_0 --config sr --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4 --dataset speech-recognition-community-v2/dev\_data --config sr --split validation --chunk\_length\_s 10 --stride\_length\_s 1 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0003 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 800 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.2 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-vot-final-a2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - VOT dataset. It achieves the following results on the evaluation set: - Loss: 2.8745 - Wer: 0.8333 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-vot-final-a2 --dataset mozilla-foundation/common_voice_8_0 --config vot --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Votic language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 340 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 11.1216 | 33.33 | 100 | 4.2848 | 1.0 | | 2.9982 | 66.67 | 200 | 2.8665 | 1.0 | | 1.5476 | 100.0 | 300 | 2.3022 | 0.8889 | | 0.2776 | 133.33 | 400 | 2.7480 | 0.8889 | | 0.1136 | 166.67 | 500 | 2.5383 | 0.8889 | | 0.0489 | 200.0 | 600 | 2.8745 | 0.8333 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0

{"language": ["vot"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "vot", "robust-speech-event", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-vot-final-a2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "vot"}, "metrics": [{"type": "wer", "value": 0.8333333333333334, "name": "Test WER"}, {"type": "cer", "value": 0.48672566371681414, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "vot"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-large-xls-r-300m-vot-final-a2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #vot #robust-speech-event #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

wav2vec2-large-xls-r-300m-vot-final-a2 ====================================== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - VOT dataset. It achieves the following results on the evaluation set: * Loss: 2.8745 * Wer: 0.8333 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-vot-final-a2 --dataset mozilla-foundation/common\_voice\_8\_0 --config vot --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Votic language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0004 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 340 * num\_epochs: 200 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.16.2 * Pytorch 1.10.0+cu111 * Datasets 1.18.3 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - KK dataset. It achieves the following results on the evaluation set: - Loss: 0.7149 - Wer: 0.451 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-kk-n2 --dataset mozilla-foundation/common_voice_8_0 --config kk --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Kazakh language not found in speech-recognition-community-v2/dev_data! ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000222 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 150.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 9.6799 | 9.09 | 200 | 3.6119 | 1.0 | | 3.1332 | 18.18 | 400 | 2.5352 | 1.005 | | 1.0465 | 27.27 | 600 | 0.6169 | 0.682 | | 0.3452 | 36.36 | 800 | 0.6572 | 0.607 | | 0.2575 | 45.44 | 1000 | 0.6527 | 0.578 | | 0.2088 | 54.53 | 1200 | 0.6828 | 0.551 | | 0.158 | 63.62 | 1400 | 0.7074 | 0.5575 | | 0.1309 | 72.71 | 1600 | 0.6523 | 0.5595 | | 0.1074 | 81.8 | 1800 | 0.7262 | 0.5415 | | 0.087 | 90.89 | 2000 | 0.7199 | 0.521 | | 0.0711 | 99.98 | 2200 | 0.7113 | 0.523 | | 0.0601 | 109.09 | 2400 | 0.6863 | 0.496 | | 0.0451 | 118.18 | 2600 | 0.6998 | 0.483 | | 0.0378 | 127.27 | 2800 | 0.6971 | 0.4615 | | 0.0319 | 136.36 | 3000 | 0.7119 | 0.4475 | | 0.0305 | 145.44 | 3200 | 0.7181 | 0.459 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["kk"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "kk", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-kk-n2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "tt"}, "metrics": [{"type": "wer", "value": 0.4355, "name": "Test WER"}, {"type": "cer", "value": 0.10469915859660263, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "vot"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-xls-r-300m-kk-n2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #kk #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - KK dataset. It achieves the following results on the evaluation set: * Loss: 0.7149 * Wer: 0.451 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-xls-r-300m-kk-n2 --dataset mozilla-foundation/common\_voice\_8\_0 --config kk --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Kazakh language not found in speech-recognition-community-v2/dev\_data! ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000222 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 150.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MT dataset. It achieves the following results on the evaluation set: - Loss: 0.1987 - Wer: 0.1920 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-mt-o1 --dataset mozilla-foundation/common_voice_8_0 --config mt --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Maltese language not found in speech-recognition-community-v2/dev_data! ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.1721 | 18.02 | 2000 | 0.3831 | 0.4066 | | 0.7849 | 36.04 | 4000 | 0.2191 | 0.2417 | | 0.6723 | 54.05 | 6000 | 0.2056 | 0.2134 | | 0.6015 | 72.07 | 8000 | 0.2008 | 0.2031 | | 0.5386 | 90.09 | 10000 | 0.1967 | 0.1953 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["mt"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "mt", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-mt-o1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "mt"}, "metrics": [{"type": "wer", "value": 0.2378369069146646, "name": "Test WER"}, {"type": "cer", "value": 0.050364163712536256, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "mt"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-xls-r-300m-mt-o1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #mt #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - MT dataset. It achieves the following results on the evaluation set: * Loss: 0.1987 * Wer: 0.1920 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-xls-r-300m-mt-o1 --dataset mozilla-foundation/common\_voice\_8\_0 --config mt --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Maltese language not found in speech-recognition-community-v2/dev\_data! ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7e-05 * train\_batch\_size: 32 * eval\_batch\_size: 1 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 2000 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset. It achieves the following results on the evaluation set: - Loss: 0.8881 - Wer: 0.4175 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-pa-IN-r5 --dataset mozilla-foundation/common_voice_8_0 --config pa-IN --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Punjabi language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.000111 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 200.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 10.695 | 18.52 | 500 | 3.5681 | 1.0 | | 3.2718 | 37.04 | 1000 | 2.3081 | 0.9643 | | 0.8727 | 55.56 | 1500 | 0.7227 | 0.5147 | | 0.3349 | 74.07 | 2000 | 0.7498 | 0.4959 | | 0.2134 | 92.59 | 2500 | 0.7779 | 0.4720 | | 0.1445 | 111.11 | 3000 | 0.8120 | 0.4594 | | 0.1057 | 129.63 | 3500 | 0.8225 | 0.4610 | | 0.0826 | 148.15 | 4000 | 0.8307 | 0.4351 | | 0.0639 | 166.67 | 4500 | 0.8967 | 0.4316 | | 0.0528 | 185.19 | 5000 | 0.8875 | 0.4238 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["pa-IN"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "pa-IN", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-pa-IN-r5", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "pa-IN"}, "metrics": [{"type": "wer", "value": 0.4186593492747942, "name": "Test WER"}, {"type": "cer", "value": 0.13301322550753938, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "pa-IN"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-xls-r-300m-pa-IN-r5

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #pa-IN #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PA-IN dataset. It achieves the following results on the evaluation set: * Loss: 0.8881 * Wer: 0.4175 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-xls-r-300m-pa-IN-r5 --dataset mozilla-foundation/common\_voice\_8\_0 --config pa-IN --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Punjabi language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.000111 * train\_batch\_size: 16 * eval\_batch\_size: 32 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 2000 * num\_epochs: 200.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - RM-SURSILV dataset. It achieves the following results on the evaluation set: - Loss: 0.2511 - Wer: 0.2415 #### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11 --dataset mozilla-foundation/common_voice_8_0 --config rm-sursilv --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Romansh-Sursilv language isn't available in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 125.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 2.3958 | 17.44 | 1500 | 0.6808 | 0.6521 | | 0.9663 | 34.88 | 3000 | 0.3023 | 0.3718 | | 0.7963 | 52.33 | 4500 | 0.2588 | 0.3046 | | 0.6893 | 69.77 | 6000 | 0.2436 | 0.2718 | | 0.6148 | 87.21 | 7500 | 0.2521 | 0.2572 | | 0.5556 | 104.65 | 9000 | 0.2490 | 0.2442 | | 0.5258 | 122.09 | 10500 | 0.2515 | 0.2442 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["rm-sursilv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "hf-asr-leaderboard", "robust-speech-event"], "datasets": ["mozilla-foundation/common_voice_8_0"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-xls-r-300m-rm-sursilv-d11", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "rm-sursilv"}, "metrics": [{"type": "wer", "value": 0.24094169578811844, "name": "Test WER"}, {"type": "cer", "value": 0.049832791672554284, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "rm-sursilv"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #hf-asr-leaderboard #robust-speech-event #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - RM-SURSILV dataset. It achieves the following results on the evaluation set: * Loss: 0.2511 * Wer: 0.2415 #### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-xls-r-300m-rm-sursilv-d11 --dataset mozilla-foundation/common\_voice\_8\_0 --config rm-sursilv --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Romansh-Sursilv language isn't available in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7e-05 * train\_batch\_size: 32 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 2000 * num\_epochs: 125.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - RM-VALLADER dataset. It achieves the following results on the evaluation set: - Loss: 0.2754 - Wer: 0.2831 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-300m-rm-vallader-d1 --dataset mozilla-foundation/common_voice_8_0 --config rm-vallader --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Romansh-Vallader language not found in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.5e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 2.927 | 15.15 | 500 | 2.9196 | 1.0 | | 1.3835 | 30.3 | 1000 | 0.5879 | 0.5866 | | 0.7415 | 45.45 | 1500 | 0.3077 | 0.3316 | | 0.5575 | 60.61 | 2000 | 0.2735 | 0.2954 | | 0.4581 | 75.76 | 2500 | 0.2707 | 0.2802 | | 0.3977 | 90.91 | 3000 | 0.2785 | 0.2809 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["rm-vallader"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "rm-vallader", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-300m-rm-vallader-d1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "rm-vallader"}, "metrics": [{"type": "wer", "value": 0.26472007722007723, "name": "Test WER"}, {"type": "cer", "value": 0.05860608074430969, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "vot"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-xls-r-300m-rm-vallader-d1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #rm-vallader #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - RM-VALLADER dataset. It achieves the following results on the evaluation set: * Loss: 0.2754 * Wer: 0.2831 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-xls-r-300m-rm-vallader-d1 --dataset mozilla-foundation/common\_voice\_8\_0 --config rm-vallader --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Romansh-Vallader language not found in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7.5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - MYV dataset. It achieves the following results on the evaluation set: - Loss: 1.0356 - Wer: 0.6524 ### Evaluation Commands **1. To evaluate on mozilla-foundation/common_voice_8_0 with test split** python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-myv-a1 --dataset mozilla-foundation/common_voice_8_0 --config myv --split test --log_outputs **2. To evaluate on speech-recognition-community-v2/dev_data** Erzya language not found in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0004 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 800 - num_epochs: 200.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | 5.649 | 9.62 | 500 | 3.0038 | 1.0 | | 1.6272 | 19.23 | 1000 | 0.7362 | 0.7819 | | 1.1354 | 28.85 | 1500 | 0.6410 | 0.7111 | | 1.0424 | 38.46 | 2000 | 0.6907 | 0.7431 | | 0.9293 | 48.08 | 2500 | 0.7249 | 0.7102 | | 0.8246 | 57.69 | 3000 | 0.7422 | 0.6966 | | 0.7837 | 67.31 | 3500 | 0.7413 | 0.6813 | | 0.7147 | 76.92 | 4000 | 0.7873 | 0.6930 | | 0.6276 | 86.54 | 4500 | 0.8038 | 0.6677 | | 0.6041 | 96.15 | 5000 | 0.8240 | 0.6831 | | 0.5336 | 105.77 | 5500 | 0.8748 | 0.6749 | | 0.4705 | 115.38 | 6000 | 0.9006 | 0.6497 | | 0.43 | 125.0 | 6500 | 0.8954 | 0.6551 | | 0.3859 | 134.62 | 7000 | 0.9074 | 0.6614 | | 0.3342 | 144.23 | 7500 | 0.9693 | 0.6560 | | 0.3155 | 153.85 | 8000 | 1.0073 | 0.6691 | | 0.2673 | 163.46 | 8500 | 1.0170 | 0.6632 | | 0.2409 | 173.08 | 9000 | 1.0304 | 0.6709 | | 0.2189 | 182.69 | 9500 | 0.9965 | 0.6546 | | 0.1973 | 192.31 | 10000 | 1.0360 | 0.6551 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0 ### Evaluation Command !python eval.py \ --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-myv-v1 \ --dataset mozilla-foundation/common_voice_8_0 --config myv --split test --log_outputs

{"language": ["myv"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "myv", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-myv-a1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "myv"}, "metrics": [{"type": "wer", "value": 0.6514672686230248, "name": "Test WER"}, {"type": "cer", "value": 0.17226131905088124, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "vot"}, "metrics": [{"type": "wer", "value": "NA", "name": "Test WER"}, {"type": "cer", "value": "NA", "name": "Test CER"}]}]}]}

DrishtiSharma/wav2vec2-xls-r-myv-a1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #myv #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - MYV dataset. It achieves the following results on the evaluation set: * Loss: 1.0356 * Wer: 0.6524 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-xls-r-myv-a1 --dataset mozilla-foundation/common\_voice\_8\_0 --config myv --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Erzya language not found in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0004 * train\_batch\_size: 16 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 800 * num\_epochs: 200.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0 ### Evaluation Command !python URL --model\_id DrishtiSharma/wav2vec2-large-xls-r-300m-myv-v1 --dataset mozilla-foundation/common\_voice\_8\_0 --config myv --split test --log\_outputs

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - PA-IN dataset. It achieves the following results on the evaluation set: - Loss: 1.1508 - Wer: 0.4908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1500 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.5841 | 9.26 | 500 | 3.2514 | 0.9941 | | 0.3992 | 18.52 | 1000 | 0.8790 | 0.6107 | | 0.2409 | 27.78 | 1500 | 1.0012 | 0.6366 | | 0.1447 | 37.04 | 2000 | 1.0167 | 0.6276 | | 0.1109 | 46.3 | 2500 | 1.0638 | 0.5653 | | 0.0797 | 55.56 | 3000 | 1.1447 | 0.5715 | | 0.0636 | 64.81 | 3500 | 1.1503 | 0.5316 | | 0.0466 | 74.07 | 4000 | 1.2227 | 0.5386 | | 0.0372 | 83.33 | 4500 | 1.1214 | 0.5225 | | 0.0239 | 92.59 | 5000 | 1.1375 | 0.4998 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["pa-IN"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]}

DrishtiSharma/wav2vec2-xls-r-pa-IN-a1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - PA-IN dataset. It achieves the following results on the evaluation set: * Loss: 1.1508 * Wer: 0.4908 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0003 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1500 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset. It achieves the following results on the evaluation set: - Loss: 0.2756 - Wer: 0.2279 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-sl-a1 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-sl-a1 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7.1e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.3881 | 6.1 | 500 | 2.9710 | 1.0 | | 2.6401 | 12.2 | 1000 | 1.7677 | 0.9734 | | 1.5152 | 18.29 | 1500 | 0.5564 | 0.6011 | | 1.2191 | 24.39 | 2000 | 0.4319 | 0.4390 | | 1.0237 | 30.49 | 2500 | 0.3141 | 0.3175 | | 0.8892 | 36.59 | 3000 | 0.2748 | 0.2689 | | 0.8296 | 42.68 | 3500 | 0.2680 | 0.2534 | | 0.7602 | 48.78 | 4000 | 0.2820 | 0.2506 | | 0.7186 | 54.88 | 4500 | 0.2672 | 0.2398 | | 0.6887 | 60.98 | 5000 | 0.2729 | 0.2402 | | 0.6507 | 67.07 | 5500 | 0.2767 | 0.2361 | | 0.6226 | 73.17 | 6000 | 0.2817 | 0.2332 | | 0.6024 | 79.27 | 6500 | 0.2679 | 0.2279 | | 0.5787 | 85.37 | 7000 | 0.2837 | 0.2316 | | 0.5744 | 91.46 | 7500 | 0.2838 | 0.2284 | | 0.5556 | 97.56 | 8000 | 0.2763 | 0.2281 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["sl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "generated_from_trainer", "hf-asr-leaderboard", "model_for_talk", "mozilla-foundation/common_voice_8_0", "robust-speech-event", "sl"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-sl-a1", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sl"}, "metrics": [{"type": "wer", "value": 0.20626555409164105, "name": "Test WER"}, {"type": "cer", "value": 0.051648321634392154, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "sl"}, "metrics": [{"type": "wer", "value": 0.5406156320830592, "name": "Test WER"}, {"type": "cer", "value": 0.22249723590310583, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "sl"}, "metrics": [{"type": "wer", "value": 55.24, "name": "Test WER"}]}]}]}

DrishtiSharma/wav2vec2-xls-r-sl-a1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #hf-asr-leaderboard #model_for_talk #mozilla-foundation/common_voice_8_0 #robust-speech-event #sl #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SL dataset. It achieves the following results on the evaluation set: * Loss: 0.2756 * Wer: 0.2279 ### Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-xls-r-sl-a1 --dataset mozilla-foundation/common\_voice\_8\_0 --config sl --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data python URL --model\_id DrishtiSharma/wav2vec2-xls-r-sl-a1 --dataset speech-recognition-community-v2/dev\_data --config sl --split validation --chunk\_length\_s 10 --stride\_length\_s 1 ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7.1e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset. It achieves the following results on the evaluation set: - Loss: 0.2855 - Wer: 0.2401 ##Evaluation Commands 1. To evaluate on mozilla-foundation/common_voice_8_0 with test split python eval.py --model_id DrishtiSharma/wav2vec2-xls-r-sl-a2 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs 2. To evaluate on speech-recognition-community-v2/dev_data Votic language not found in speech-recognition-community-v2/dev_data ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.9294 | 6.1 | 500 | 2.9712 | 1.0 | | 2.8305 | 12.2 | 1000 | 1.7073 | 0.9479 | | 1.4795 | 18.29 | 1500 | 0.5756 | 0.6397 | | 1.3433 | 24.39 | 2000 | 0.4968 | 0.5424 | | 1.1766 | 30.49 | 2500 | 0.4185 | 0.4743 | | 1.0017 | 36.59 | 3000 | 0.3303 | 0.3578 | | 0.9358 | 42.68 | 3500 | 0.3003 | 0.3051 | | 0.8358 | 48.78 | 4000 | 0.3045 | 0.2884 | | 0.7647 | 54.88 | 4500 | 0.2866 | 0.2677 | | 0.7482 | 60.98 | 5000 | 0.2829 | 0.2585 | | 0.6943 | 67.07 | 5500 | 0.2782 | 0.2478 | | 0.6586 | 73.17 | 6000 | 0.2911 | 0.2537 | | 0.6425 | 79.27 | 6500 | 0.2817 | 0.2462 | | 0.6067 | 85.37 | 7000 | 0.2910 | 0.2436 | | 0.5974 | 91.46 | 7500 | 0.2875 | 0.2430 | | 0.5812 | 97.56 | 8000 | 0.2852 | 0.2396 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

{"language": ["sl"], "license": "apache-2.0", "tags": ["automatic-speech-recognition", "mozilla-foundation/common_voice_8_0", "generated_from_trainer", "sl", "robust-speech-event", "model_for_talk", "hf-asr-leaderboard"], "datasets": ["mozilla-foundation/common_voice_8_0"], "model-index": [{"name": "wav2vec2-xls-r-sl-a2", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "args": "sl"}, "metrics": [{"type": "wer", "value": 0.21695212999560826, "name": "Test WER"}, {"type": "cer", "value": 0.052850080572474256, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Dev Data", "type": "speech-recognition-community-v2/dev_data", "args": "vot"}, "metrics": [{"type": "wer", "value": 0.560722380639029, "name": "Test WER"}, {"type": "cer", "value": 0.2279626093074681, "name": "Test CER"}, {"type": "wer", "value": 56.07, "name": "Test WER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Robust Speech Event - Test Data", "type": "speech-recognition-community-v2/eval_data", "args": "sl"}, "metrics": [{"type": "wer", "value": 56.19, "name": "Test WER"}]}]}]}

DrishtiSharma/wav2vec2-xls-r-sl-a2

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #mozilla-foundation/common_voice_8_0 #generated_from_trainer #sl #robust-speech-event #model_for_talk #hf-asr-leaderboard #dataset-mozilla-foundation/common_voice_8_0 #license-apache-2.0 #model-index #endpoints_compatible #region-us

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON\_VOICE\_8\_0 - SL dataset. It achieves the following results on the evaluation set: * Loss: 0.2855 * Wer: 0.2401 ##Evaluation Commands 1. To evaluate on mozilla-foundation/common\_voice\_8\_0 with test split python URL --model\_id DrishtiSharma/wav2vec2-xls-r-sl-a2 --dataset mozilla-foundation/common\_voice\_8\_0 --config sl --split test --log\_outputs 2. To evaluate on speech-recognition-community-v2/dev\_data Votic language not found in speech-recognition-community-v2/dev\_data ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 7e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 100.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.17.0.dev0 * Pytorch 1.10.2+cu102 * Datasets 1.18.2.dev0 * Tokenizers 0.11.0

token-classification

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Precision: 0.9262 - Recall: 0.9375 - F1: 0.9318 - Accuracy: 0.9841 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.2424 | 1.0 | 878 | 0.0684 | 0.9096 | 0.9206 | 0.9150 | 0.9813 | | 0.0524 | 2.0 | 1756 | 0.0607 | 0.9188 | 0.9349 | 0.9268 | 0.9835 | | 0.0304 | 3.0 | 2634 | 0.0604 | 0.9262 | 0.9375 | 0.9318 | 0.9841 | ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9261715296198055, "name": "Precision"}, {"type": "recall", "value": 0.9374650408323079, "name": "Recall"}, {"type": "f1", "value": 0.9317840662700839, "name": "F1"}, {"type": "accuracy", "value": 0.9840659602522758, "name": "Accuracy"}]}]}]}

Duc/distilbert-base-uncased-finetuned-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-conll2003 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

distilbert-base-uncased-finetuned-ner ===================================== This model is a fine-tuned version of distilbert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set: * Loss: 0.0604 * Precision: 0.9262 * Recall: 0.9375 * F1: 0.9318 * Accuracy: 0.9841 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.12.3 * Pytorch 1.9.0+cu111 * Datasets 1.15.1 * Tokenizers 0.10.3

text-generation

transformers

# Harry Potter DialoGPT Model

{"tags": ["conversational"]}

DueLinx0402/DialoGPT-small-harrypotter

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Harry Potter DialoGPT Model

automatic-speech-recognition

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> ## This model achieves WER on common-voice ro test split of WER: 12.457178% # wav2vec2-xls-r-300m-romanian This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an common voice ro and RSS dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0836 - eval_wer: 0.0705 - eval_runtime: 160.4549 - eval_samples_per_second: 11.081 - eval_steps_per_second: 1.39 - epoch: 14.38 - step: 2703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 15 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3 Used the following code for evaluation: ``` import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "ro", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("Dumiiii/wav2vec2-xls-r-300m-romanian") model = Wav2Vec2ForCTC.from_pretrained("Dumiiii/wav2vec2-xls-r-300m-romanian") model.to("cuda") chars_to_ignore_regex = '['+string.punctuation+']' resampler = torchaudio.transforms.Resample(48_000, 16_000) # Preprocessing the datasets. # We need to read the aduio files as arrays def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(speech_array).squeeze().numpy() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) # Preprocessing the datasets. # We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` Credits for evaluation: https://huggingface.co/anton-l

{"license": "apache-2.0", "tags": ["generated_from_trainer"]}

Dumiiii/wav2vec2-xls-r-300m-romanian

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #tensorboard #wav2vec2 #automatic-speech-recognition #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us

## This model achieves WER on common-voice ro test split of WER: 12.457178% # wav2vec2-xls-r-300m-romanian This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an common voice ro and RSS dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0836 - eval_wer: 0.0705 - eval_runtime: 160.4549 - eval_samples_per_second: 11.081 - eval_steps_per_second: 1.39 - epoch: 14.38 - step: 2703 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 15 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3 Used the following code for evaluation: Credits for evaluation: URL

text-generation

transformers

# Alexia Bot Testing

{}

Duugu/alexia-bot-test

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt_neo #text-generation #autotrain_compatible #endpoints_compatible #region-us

# Alexia Bot Testing

text-generation

transformers

# My Awesome Model

{"tags": ["conversational"]}

Duugu/jakebot3000

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# My Awesome Model

text-generation

transformers

#Landcheese

{"tags": ["conversational"]}

Dyzi/DialoGPT-small-landcheese

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

#Landcheese

text2text-generation

transformers

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # out This model is a fine-tuned version of [/1TB_SSD/SB_AI/out_epoch1/out/checkpoint-1115000/](https://huggingface.co//1TB_SSD/SB_AI/out_epoch1/out/checkpoint-1115000/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0645 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 2518227880 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-------:|:---------------:| | 0.0867 | 0.07 | 75000 | 0.0742 | | 0.0783 | 0.13 | 150000 | 0.0695 | | 0.0719 | 0.2 | 225000 | 0.0732 | | 0.0743 | 0.27 | 300000 | 0.0663 | | 0.0659 | 0.34 | 375000 | 0.0686 | | 0.0664 | 0.4 | 450000 | 0.0683 | | 0.0637 | 0.47 | 525000 | 0.0680 | | 0.0655 | 0.54 | 600000 | 0.0641 | | 0.0676 | 0.6 | 675000 | 0.0644 | | 0.0704 | 0.67 | 750000 | 0.0645 | | 0.0687 | 0.74 | 825000 | 0.0610 | | 0.059 | 0.81 | 900000 | 0.0652 | | 0.0666 | 0.87 | 975000 | 0.0619 | | 0.0624 | 0.94 | 1050000 | 0.0619 | | 0.0625 | 1.01 | 1125000 | 0.0667 | | 0.0614 | 1.03 | 1150000 | 0.0658 | | 0.0597 | 1.05 | 1175000 | 0.0683 | | 0.0629 | 1.07 | 1200000 | 0.0691 | | 0.0603 | 1.1 | 1225000 | 0.0678 | | 0.0601 | 1.12 | 1250000 | 0.0746 | | 0.0606 | 1.14 | 1275000 | 0.0691 | | 0.0671 | 1.16 | 1300000 | 0.0702 | | 0.0625 | 1.19 | 1325000 | 0.0661 | | 0.0617 | 1.21 | 1350000 | 0.0688 | | 0.0579 | 1.23 | 1375000 | 0.0679 | | 0.0663 | 1.25 | 1400000 | 0.0634 | | 0.0583 | 1.28 | 1425000 | 0.0638 | | 0.0623 | 1.3 | 1450000 | 0.0681 | | 0.0615 | 1.32 | 1475000 | 0.0670 | | 0.0592 | 1.34 | 1500000 | 0.0666 | | 0.0626 | 1.37 | 1525000 | 0.0666 | | 0.063 | 1.39 | 1550000 | 0.0647 | | 0.0648 | 1.41 | 1575000 | 0.0653 | | 0.0611 | 1.43 | 1600000 | 0.0700 | | 0.0622 | 1.46 | 1625000 | 0.0634 | | 0.0617 | 1.48 | 1650000 | 0.0651 | | 0.0613 | 1.5 | 1675000 | 0.0634 | | 0.0639 | 1.52 | 1700000 | 0.0661 | | 0.0615 | 1.54 | 1725000 | 0.0644 | | 0.0605 | 1.57 | 1750000 | 0.0662 | | 0.0622 | 1.59 | 1775000 | 0.0656 | | 0.0585 | 1.61 | 1800000 | 0.0633 | | 0.0628 | 1.63 | 1825000 | 0.0625 | | 0.0638 | 1.66 | 1850000 | 0.0662 | | 0.0599 | 1.68 | 1875000 | 0.0664 | | 0.0583 | 1.7 | 1900000 | 0.0668 | | 0.0543 | 1.72 | 1925000 | 0.0631 | | 0.06 | 1.75 | 1950000 | 0.0629 | | 0.0615 | 1.77 | 1975000 | 0.0644 | | 0.0587 | 1.79 | 2000000 | 0.0663 | | 0.0647 | 1.81 | 2025000 | 0.0654 | | 0.0604 | 1.84 | 2050000 | 0.0639 | | 0.0641 | 1.86 | 2075000 | 0.0636 | | 0.0604 | 1.88 | 2100000 | 0.0636 | | 0.0654 | 1.9 | 2125000 | 0.0652 | | 0.0588 | 1.93 | 2150000 | 0.0638 | | 0.0616 | 1.95 | 2175000 | 0.0657 | | 0.0598 | 1.97 | 2200000 | 0.0646 | | 0.0633 | 1.99 | 2225000 | 0.0645 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.17.0 - Tokenizers 0.10.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "out", "results": []}]}

EColi/sponsorblock-base-v1

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us

out === This model is a fine-tuned version of /1TB\_SSD/SB\_AI/out\_epoch1/out/checkpoint-1115000/ on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0645 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 1 * eval\_batch\_size: 1 * seed: 2518227880 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2.0 ### Training results ### Framework versions * Transformers 4.15.0 * Pytorch 1.10.1+cu113 * Datasets 1.17.0 * Tokenizers 0.10.3

text-generation

transformers

# Brooke DialoGPT Model

{"tags": ["conversational"]}

EEE/DialoGPT-medium-brooke

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Brooke DialoGPT Model

text-generation

transformers

# Aang DialoGPT Model

{"tags": ["conversational"]}

EEE/DialoGPT-small-aang

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Aang DialoGPT Model

text-generation

transformers

# Yoda DialoGPT Model

{"tags": ["conversational"]}

EEE/DialoGPT-small-yoda

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Yoda DialoGPT Model

summarization

transformers

**IMPORTANT:** On the 5th of April 2022, we detected a mistake in the configuration file; thus, the model was not generating the summaries correctly, and it was underperforming in all scenarios. For this reason, if you had used the model until that day, we would be glad if you would re-evaluate the model if you are publishing some results with it. We apologize for the inconvenience and thank you for your understanding. # NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially for minority languages like Catalan. In this paper, we present a monolingual model for abstractive summarization of textual content in the Catalan language. The model is a Transformer encoder-decoder which is pretrained and fine-tuned specifically for the Catalan language using a corpus of newspaper articles. In the pretraining phase, we introduced several self-supervised tasks to specialize the model on the summarization task and to increase the abstractivity of the generated summaries. To study the performance of our proposal in languages with higher resources than Catalan, we replicate the model and the experimentation for the Spanish language. The usual evaluation metrics, not only the most used ROUGE measure but also other more semantic ones such as BertScore, do not allow to correctly evaluate the abstractivity of the generated summaries. In this work, we also present a new metric, called content reordering, to evaluate one of the most common characteristics of abstractive summaries, the rearrangement of the original content. We carried out an exhaustive experimentation to compare the performance of the monolingual models proposed in this work with two of the most widely used multilingual models in text summarization, mBART and mT5. The experimentation results support the quality of our monolingual models, especially considering that the multilingual models were pretrained with many more resources than those used in our models. Likewise, it is shown that the pretraining tasks helped to increase the degree of abstractivity of the generated summaries. To our knowledge, this is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish. # The NASca model News Abstractive Summarization for Catalan (NASca) is a Transformer encoder-decoder model, with the same hyper-parameters than BART, to perform summarization of Catalan news articles. It is pre-trained on a combination of several self-supervised tasks that help to increase the abstractivity of the generated summaries. Four pre-training tasks have been combined: sentence permutation, text infilling, Gap Sentence Generation, and Next Segment Generation. Catalan newspapers, the Catalan subset of the OSCAR corpus and Wikipedia articles in Catalan were used for pre-training the model (9.3GB of raw text -2.5 millions of documents-). NASca is finetuned for the summarization task on 636.596 (document, summary) pairs from the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA). ### BibTeX entry ```bibtex @Article{app11219872, AUTHOR = {Ahuir, Vicent and Hurtado, Lluís-F. and González, José Ángel and Segarra, Encarna}, TITLE = {NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish}, JOURNAL = {Applied Sciences}, VOLUME = {11}, YEAR = {2021}, NUMBER = {21}, ARTICLE-NUMBER = {9872}, URL = {https://www.mdpi.com/2076-3417/11/21/9872}, ISSN = {2076-3417}, DOI = {10.3390/app11219872} } ```

{"language": "ca", "tags": ["summarization"], "widget": [{"text": "La Universitat Polit\u00e8cnica de Val\u00e8ncia (UPV), a trav\u00e9s del projecte Atenea \u201cplataforma de dones, art i tecnologia\u201d i en col\u00b7laboraci\u00f3 amb les companyies tecnol\u00f2giques Metric Salad i Zetalab, ha digitalitzat i modelat en 3D per a la 35a edici\u00f3 del Festival Dansa Val\u00e8ncia, que se celebra del 2 al 10 d'abril, la primera pe\u00e7a de dansa en un metaverso espec\u00edfic. La pe\u00e7a No \u00e9s amor, dirigida per Lara Mis\u00f3, forma part de la programaci\u00f3 d'aquesta edici\u00f3 del Festival Dansa Val\u00e8ncia i explora la figura geom\u00e8trica del cercle des de totes les seues perspectives: espacial, corporal i compositiva. No \u00e9s amor est\u00e0 inspirada en el treball de l'artista japonesa Yayoi Kusama i mira de prop les diferents facetes d'una obsessi\u00f3. Aix\u00ed dona cabuda a la insist\u00e8ncia, la repetici\u00f3, el trastorn, la hipnosi i l'alliberament. El proc\u00e9s de digitalitzaci\u00f3, materialitzat per Metric Salad i ZetaLab, ha sigut complex respecte a uns altres ja realitzats a causa de l'enorme desafiament que comporta el modelatge en 3D de cossos en moviment al ritme de la composici\u00f3 de l'obra. L'objectiu era generar una experi\u00e8ncia el m\u00e9s realista possible i fidedigna de l'original perqu\u00e8 el resultat final fora un proc\u00e9s absolutament immersiu.Aix\u00ed, el metaverso est\u00e0 compost per figures modelades en 3D al costat de quatre projeccions digitalitzades en pantalles flotants amb les quals l'usuari podr\u00e0 interactuar segons es vaja acostant, b\u00e9 mitjan\u00e7ant els comandaments de l'ordinador, b\u00e9 a trav\u00e9s d'ulleres de realitat virtual. L'objectiu \u00e9s que quan l'usuari s'acoste a cadascuna de les projeccions tinga la sensaci\u00f3 d'una immersi\u00f3 quasi completa en fondre's amb el contingut audiovisual que li genere una experi\u00e8ncia intimista i molt real."}]}

ELiRF/NASCA

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #bart #text2text-generation #summarization #ca #autotrain_compatible #endpoints_compatible #region-us

IMPORTANT: On the 5th of April 2022, we detected a mistake in the configuration file; thus, the model was not generating the summaries correctly, and it was underperforming in all scenarios. For this reason, if you had used the model until that day, we would be glad if you would re-evaluate the model if you are publishing some results with it. We apologize for the inconvenience and thank you for your understanding. # NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially for minority languages like Catalan. In this paper, we present a monolingual model for abstractive summarization of textual content in the Catalan language. The model is a Transformer encoder-decoder which is pretrained and fine-tuned specifically for the Catalan language using a corpus of newspaper articles. In the pretraining phase, we introduced several self-supervised tasks to specialize the model on the summarization task and to increase the abstractivity of the generated summaries. To study the performance of our proposal in languages with higher resources than Catalan, we replicate the model and the experimentation for the Spanish language. The usual evaluation metrics, not only the most used ROUGE measure but also other more semantic ones such as BertScore, do not allow to correctly evaluate the abstractivity of the generated summaries. In this work, we also present a new metric, called content reordering, to evaluate one of the most common characteristics of abstractive summaries, the rearrangement of the original content. We carried out an exhaustive experimentation to compare the performance of the monolingual models proposed in this work with two of the most widely used multilingual models in text summarization, mBART and mT5. The experimentation results support the quality of our monolingual models, especially considering that the multilingual models were pretrained with many more resources than those used in our models. Likewise, it is shown that the pretraining tasks helped to increase the degree of abstractivity of the generated summaries. To our knowledge, this is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish. # The NASca model News Abstractive Summarization for Catalan (NASca) is a Transformer encoder-decoder model, with the same hyper-parameters than BART, to perform summarization of Catalan news articles. It is pre-trained on a combination of several self-supervised tasks that help to increase the abstractivity of the generated summaries. Four pre-training tasks have been combined: sentence permutation, text infilling, Gap Sentence Generation, and Next Segment Generation. Catalan newspapers, the Catalan subset of the OSCAR corpus and Wikipedia articles in Catalan were used for pre-training the model (9.3GB of raw text -2.5 millions of documents-). NASca is finetuned for the summarization task on 636.596 (document, summary) pairs from the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA). ### BibTeX entry

summarization

transformers

**IMPORTANT:** On the 5th of April 2022, we detected a mistake in the configuration file; thus, the model was not generating the summaries correctly, and it was underperforming in all scenarios. For this reason, if you had used the model until that day, we would be glad if you would re-evaluate the model if you are publishing some results with it. We apologize for the inconvenience and thank you for your understanding. # NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially for minority languages like Catalan. In this paper, we present a monolingual model for abstractive summarization of textual content in the Catalan language. The model is a Transformer encoder-decoder which is pretrained and fine-tuned specifically for the Catalan language using a corpus of newspaper articles. In the pretraining phase, we introduced several self-supervised tasks to specialize the model on the summarization task and to increase the abstractivity of the generated summaries. To study the performance of our proposal in languages with higher resources than Catalan, we replicate the model and the experimentation for the Spanish language. The usual evaluation metrics, not only the most used ROUGE measure but also other more semantic ones such as BertScore, do not allow to correctly evaluate the abstractivity of the generated summaries. In this work, we also present a new metric, called content reordering, to evaluate one of the most common characteristics of abstractive summaries, the rearrangement of the original content. We carried out an exhaustive experimentation to compare the performance of the monolingual models proposed in this work with two of the most widely used multilingual models in text summarization, mBART and mT5. The experimentation results support the quality of our monolingual models, especially considering that the multilingual models were pretrained with many more resources than those used in our models. Likewise, it is shown that the pretraining tasks helped to increase the degree of abstractivity of the generated summaries. To our knowledge, this is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish. # The NASes model News Abstractive Summarization for Spanish (NASes) is a Transformer encoder-decoder model, with the same hyper-parameters than BART, to perform summarization of Spanish news articles. It is pre-trained on a combination of several self-supervised tasks that help to increase the abstractivity of the generated summaries. Four pre-training tasks have been combined: sentence permutation, text infilling, Gap Sentence Generation, and Next Segment Generation. Spanish newspapers, and Wikipedia articles in Spanish were used for pre-training the model (21GB of raw text -8.5 millions of documents-). NASes is finetuned for the summarization task on 1.802.919 (document, summary) pairs from the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA). ### BibTeX entry ```bibtex @Article{app11219872, AUTHOR = {Ahuir, Vicent and Hurtado, Lluís-F. and González, José Ángel and Segarra, Encarna}, TITLE = {NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish}, JOURNAL = {Applied Sciences}, VOLUME = {11}, YEAR = {2021}, NUMBER = {21}, ARTICLE-NUMBER = {9872}, URL = {https://www.mdpi.com/2076-3417/11/21/9872}, ISSN = {2076-3417}, DOI = {10.3390/app11219872} } ```

{"language": "es", "tags": ["summarization"], "widget": [{"text": "La Agencia Valenciana de la Innovaci\u00f3n (AVI) financia el desarrollo de un software que integra diferentes modelos y tecnolog\u00edas para la monitorizaci\u00f3n y an\u00e1lisis multiling\u00fce de las redes sociales. A trav\u00e9s de t\u00e9cnicas de 'deep learning' y procesamiento del lenguaje natural es capaz de interpretar la iron\u00eda y las emociones en los textos, incluso en aquellos escritos en idiomas menos extendidos, a menudo no contemplados por las herramientas comerciales. La iniciativa, bautizada como 'Guaita', est\u00e1 liderada por el Instituto Valenciano de Investigaci\u00f3n en Inteligencia Artificial (VRAIN), adscrito a la Universidad Polit\u00e9cnica de Valencia (UPV), que cuenta a su vez para su desarrollo con la colaboraci\u00f3n del Instituto Valenciano de Inform\u00e1tica (ITI) y la Corporaci\u00f3n Valenciana de Mitjans de Comunicaci\u00f3n (CVMC).De este modo, y a solicitud del usuario o usuaria, monitorizar\u00e1 las redes sociales para obtener la informaci\u00f3n asociada a los temas objeto de inter\u00e9s y ofrecer\u00e1 los resultados de forma gr\u00e1fica, bien a trav\u00e9s de una interfaz web, bien mediante la generaci\u00f3n de informes. El programa ser\u00e1, adem\u00e1s, capaz de determinar la reputaci\u00f3n de una empresa o instituci\u00f3n a partir de dichos an\u00e1lisis gracias a la combinaci\u00f3n de distintas tecnolog\u00edas de procesamiento e interpretaci\u00f3n, destaca la agencia en un comunicado."}]}

ELiRF/NASES

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #safetensors #bart #text2text-generation #summarization #es #autotrain_compatible #endpoints_compatible #has_space #region-us

IMPORTANT: On the 5th of April 2022, we detected a mistake in the configuration file; thus, the model was not generating the summaries correctly, and it was underperforming in all scenarios. For this reason, if you had used the model until that day, we would be glad if you would re-evaluate the model if you are publishing some results with it. We apologize for the inconvenience and thank you for your understanding. # NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish Most of the models proposed in the literature for abstractive summarization are generally suitable for the English language but not for other languages. Multilingual models were introduced to address that language constraint, but despite their applicability being broader than that of the monolingual models, their performance is typically lower, especially for minority languages like Catalan. In this paper, we present a monolingual model for abstractive summarization of textual content in the Catalan language. The model is a Transformer encoder-decoder which is pretrained and fine-tuned specifically for the Catalan language using a corpus of newspaper articles. In the pretraining phase, we introduced several self-supervised tasks to specialize the model on the summarization task and to increase the abstractivity of the generated summaries. To study the performance of our proposal in languages with higher resources than Catalan, we replicate the model and the experimentation for the Spanish language. The usual evaluation metrics, not only the most used ROUGE measure but also other more semantic ones such as BertScore, do not allow to correctly evaluate the abstractivity of the generated summaries. In this work, we also present a new metric, called content reordering, to evaluate one of the most common characteristics of abstractive summaries, the rearrangement of the original content. We carried out an exhaustive experimentation to compare the performance of the monolingual models proposed in this work with two of the most widely used multilingual models in text summarization, mBART and mT5. The experimentation results support the quality of our monolingual models, especially considering that the multilingual models were pretrained with many more resources than those used in our models. Likewise, it is shown that the pretraining tasks helped to increase the degree of abstractivity of the generated summaries. To our knowledge, this is the first work that explores a monolingual approach for abstractive summarization both in Catalan and Spanish. # The NASes model News Abstractive Summarization for Spanish (NASes) is a Transformer encoder-decoder model, with the same hyper-parameters than BART, to perform summarization of Spanish news articles. It is pre-trained on a combination of several self-supervised tasks that help to increase the abstractivity of the generated summaries. Four pre-training tasks have been combined: sentence permutation, text infilling, Gap Sentence Generation, and Next Segment Generation. Spanish newspapers, and Wikipedia articles in Spanish were used for pre-training the model (21GB of raw text -8.5 millions of documents-). NASes is finetuned for the summarization task on 1.802.919 (document, summary) pairs from the Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (DACSA). ### BibTeX entry

fill-mask

transformers

# CroSloEngual BERT CroSloEngual BERT is a trilingual model, using bert-base architecture, trained on Croatian, Slovenian, and English corpora. Focusing on three languages, the model performs better than [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased), while still offering an option for cross-lingual knowledge transfer, which a monolingual model wouldn't. Evaluation is presented in our article: ``` @Inproceedings{ulcar-robnik2020finest, author = "Ulčar, M. and Robnik-Šikonja, M.", year = 2020, title = "{FinEst BERT} and {CroSloEngual BERT}: less is more in multilingual models", editor = "Sojka, P and Kopeček, I and Pala, K and Horák, A", booktitle = "Text, Speech, and Dialogue {TSD 2020}", series = "Lecture Notes in Computer Science", volume = 12284, publisher = "Springer", url = "https://doi.org/10.1007/978-3-030-58323-1_11", } ``` The preprint is available at [arxiv.org/abs/2006.07890](https://arxiv.org/abs/2006.07890).

{"language": ["hr", "sl", "en", "multilingual"], "license": "cc-by-4.0"}

EMBEDDIA/crosloengual-bert

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #fill-mask #hr #sl #en #multilingual #arxiv-2006.07890 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

# CroSloEngual BERT CroSloEngual BERT is a trilingual model, using bert-base architecture, trained on Croatian, Slovenian, and English corpora. Focusing on three languages, the model performs better than multilingual BERT, while still offering an option for cross-lingual knowledge transfer, which a monolingual model wouldn't. Evaluation is presented in our article: The preprint is available at URL

fill-mask

transformers

# Usage Load in transformers library with: ``` from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/est-roberta") model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/est-roberta") ``` # Est-RoBERTa Est-RoBERTa model is a monolingual Estonian BERT-like model. It is closely related to French Camembert model https://camembert-model.fr/. The Estonian corpora used for training the model have 2.51 billion tokens in total. The subword vocabulary contains 40,000 tokens. Est-RoBERTa was trained for 40 epochs.

{"language": ["et"], "license": "cc-by-sa-4.0"}

EMBEDDIA/est-roberta

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #camembert #fill-mask #et #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

# Usage Load in transformers library with: # Est-RoBERTa Est-RoBERTa model is a monolingual Estonian BERT-like model. It is closely related to French Camembert model URL The Estonian corpora used for training the model have 2.51 billion tokens in total. The subword vocabulary contains 40,000 tokens. Est-RoBERTa was trained for 40 epochs.

fill-mask

transformers

# FinEst BERT FinEst BERT is a trilingual model, using bert-base architecture, trained on Finnish, Estonian, and English corpora. Focusing on three languages, the model performs better than [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased), while still offering an option for cross-lingual knowledge transfer, which a monolingual model wouldn't. Evaluation is presented in our article: ``` @Inproceedings{ulcar-robnik2020finest, author = "Ulčar, M. and Robnik-Šikonja, M.", year = 2020, title = "{FinEst BERT} and {CroSloEngual BERT}: less is more in multilingual models", editor = "Sojka, P and Kopeček, I and Pala, K and Horák, A", booktitle = "Text, Speech, and Dialogue {TSD 2020}", series = "Lecture Notes in Computer Science", volume = 12284, publisher = "Springer", url = "https://doi.org/10.1007/978-3-030-58323-1_11", } ``` The preprint is available at [arxiv.org/abs/2006.07890](https://arxiv.org/abs/2006.07890).

{"language": ["fi", "et", "en", "multilingual"], "license": "cc-by-4.0"}

EMBEDDIA/finest-bert

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #bert #fill-mask #fi #et #en #multilingual #arxiv-2006.07890 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us

# FinEst BERT FinEst BERT is a trilingual model, using bert-base architecture, trained on Finnish, Estonian, and English corpora. Focusing on three languages, the model performs better than multilingual BERT, while still offering an option for cross-lingual knowledge transfer, which a monolingual model wouldn't. Evaluation is presented in our article: The preprint is available at URL

fill-mask

transformers

# LitLat BERT LitLat BERT is a trilingual model, using xlm-roberta-base architecture, trained on Lithuanian, Latvian, and English corpora. Focusing on three languages, the model performs better than [multilingual BERT](https://huggingface.co/bert-base-multilingual-cased), while still offering an option for cross-lingual knowledge transfer, which a monolingual model wouldn't. ### Named entity recognition evaluation We compare LitLat BERT with multilingual BERT (mBERT), XLM-RoBERTa (XLM-R) and monolingual Latvian BERT (LVBERT) (Znotins and Barzdins, 2020). The report the results as a macro F1 score of 3 named entity classes shared in all three datasets: person, location, organization. Language | mBERT | XLM-R | LVBERT | LitLat ---|---|---|---|--- Latvian | 0.830 | 0.865 | 0.797 | **0.881** Lithuanian | 0.797 | 0.817 | / | **0.850** English | 0.939 | 0.937 | / | **0.943**

{"language": ["lt", "lv", "en", "multilingual"], "license": "cc-by-sa-4.0"}

EMBEDDIA/litlat-bert

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #lt #lv #en #multilingual #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

LitLat BERT =========== LitLat BERT is a trilingual model, using xlm-roberta-base architecture, trained on Lithuanian, Latvian, and English corpora. Focusing on three languages, the model performs better than multilingual BERT, while still offering an option for cross-lingual knowledge transfer, which a monolingual model wouldn't. ### Named entity recognition evaluation We compare LitLat BERT with multilingual BERT (mBERT), XLM-RoBERTa (XLM-R) and monolingual Latvian BERT (LVBERT) (Znotins and Barzdins, 2020). The report the results as a macro F1 score of 3 named entity classes shared in all three datasets: person, location, organization.

fill-mask

transformers

# Usage Load in transformers library with: ``` from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("EMBEDDIA/sloberta") model = AutoModelForMaskedLM.from_pretrained("EMBEDDIA/sloberta") ``` # SloBERTa SloBERTa model is a monolingual Slovene BERT-like model. It is closely related to French Camembert model https://camembert-model.fr/. The corpora used for training the model have 3.47 billion tokens in total. The subword vocabulary contains 32,000 tokens. The scripts and programs used for data preparation and training the model are available on https://github.com/clarinsi/Slovene-BERT-Tool SloBERTa was trained for 200,000 iterations or about 98 epochs. ## Corpora The following corpora were used for training the model: * Gigafida 2.0 * Kas 1.0 * Janes 1.0 (only Janes-news, Janes-forum, Janes-blog, Janes-wiki subcorpora) * Slovenian parliamentary corpus siParl 2.0 * slWaC

{"language": ["sl"], "license": "cc-by-sa-4.0"}

EMBEDDIA/sloberta

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #camembert #fill-mask #sl #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us

# Usage Load in transformers library with: # SloBERTa SloBERTa model is a monolingual Slovene BERT-like model. It is closely related to French Camembert model URL The corpora used for training the model have 3.47 billion tokens in total. The subword vocabulary contains 32,000 tokens. The scripts and programs used for data preparation and training the model are available on URL SloBERTa was trained for 200,000 iterations or about 98 epochs. ## Corpora The following corpora were used for training the model: * Gigafida 2.0 * Kas 1.0 * Janes 1.0 (only Janes-news, Janes-forum, Janes-blog, Janes-wiki subcorpora) * Slovenian parliamentary corpus siParl 2.0 * slWaC

fill-mask

transformers

# bio-lm ## Model description This model is a [RoBERTa base pre-trained model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). ## Intended uses & limitations #### How to use The intended use of this model is to be fine-tuned for downstream tasks, token classification in particular. To have a quick check of the model as-is in a fill-mask task: ```python from transformers import pipeline, RobertaTokenizerFast tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512) text = "Let us try this model to see if it <mask>." fill_mask = pipeline( "fill-mask", model='EMBO/bio-lm', tokenizer=tokenizer ) fill_mask(text) ``` #### Limitations and bias This model should be fine-tuned on a specifi task like token classification. The model must be used with the `roberta-base` tokenizer. ## Training data The model was trained with a masked language modeling taskon the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang) wich includes 12Mio examples from abstracts and figure legends extracted from papers published in life sciences. ## Training procedure The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs. Training code is available at https://github.com/source-data/soda-roberta - Command: `python -m lm.train /data/json/oapmc_abstracts_figs/ MLM` - Tokenizer vocab size: 50265 - Training data: EMBO/biolang MLM - Training with: 12005390 examples - Evaluating on: 36713 examples - Epochs: 3.0 - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 5e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - tensorboard run: lm-MLM-2021-01-27T15-17-43.113766 End of training: ``` trainset: 'loss': 0.8653350830078125 validation set: 'eval_loss': 0.8192330598831177, 'eval_recall': 0.8154601116513597 ``` ## Eval results Eval on test set: ``` recall: 0.814471959728645 ```

{"language": ["english"], "tags": ["language model"], "datasets": ["EMBO/biolang"], "metrics": []}

EMBO/bio-lm

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #roberta #fill-mask #language model #dataset-EMBO/biolang #autotrain_compatible #endpoints_compatible #region-us

# bio-lm ## Model description This model is a RoBERTa base pre-trained model that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the BioLang dataset. ## Intended uses & limitations #### How to use The intended use of this model is to be fine-tuned for downstream tasks, token classification in particular. To have a quick check of the model as-is in a fill-mask task: #### Limitations and bias This model should be fine-tuned on a specifi task like token classification. The model must be used with the 'roberta-base' tokenizer. ## Training data The model was trained with a masked language modeling taskon the BioLang dataset wich includes 12Mio examples from abstracts and figure legends extracted from papers published in life sciences. ## Training procedure The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs. Training code is available at URL - Command: 'python -m URL /data/json/oapmc_abstracts_figs/ MLM' - Tokenizer vocab size: 50265 - Training data: EMBO/biolang MLM - Training with: 12005390 examples - Evaluating on: 36713 examples - Epochs: 3.0 - 'per_device_train_batch_size': 16 - 'per_device_eval_batch_size': 16 - 'learning_rate': 5e-05 - 'weight_decay': 0.0 - 'adam_beta1': 0.9 - 'adam_beta2': 0.999 - 'adam_epsilon': 1e-08 - 'max_grad_norm': 1.0 - tensorboard run: lm-MLM-2021-01-27T15-17-43.113766 End of training: ## Eval results Eval on test set:

token-classification

transformers

# sd-ner ## Model description This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of English scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It was then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `NER` configuration to perform Named Entity Recognition of bioentities. ## Intended uses & limitations #### How to use The intended use of this model is for Named Entity Recognition of biological entities used in SourceData annotations (https://sourcedata.embo.org), including small molecules, gene products (genes and proteins), subcellular components, cell line and cell types, organ and tissues, species as well as experimental methods. To have a quick check of the model: ```python from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification example = """<s> F. Western blot of input and eluates of Upf1 domains purification in a Nmd4-HA strain. The band with the # might corresponds to a dimer of Upf1-CH, bands marked with a star correspond to residual signal with the anti-HA antibodies (Nmd4). Fragments in the eluate have a smaller size because the protein A part of the tag was removed by digestion with the TEV protease. G6PDH served as a loading control in the input samples </s>""" tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512) model = RobertaForTokenClassification.from_pretrained('EMBO/sd-ner') ner = pipeline('ner', model, tokenizer=tokenizer) res = ner(example) for r in res: print(r['word'], r['entity']) ``` #### Limitations and bias The model must be used with the `roberta-base` tokenizer. ## Training data The model was trained for token classification using the [EMBO/sd-nlp dataset](https://huggingface.co/datasets/EMBO/sd-nlp) dataset which includes manually annotated examples. ## Training procedure The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs. Training code is available at https://github.com/source-data/soda-roberta - Model fine-tuned: EMBO/bio-lm - Tokenizer vocab size: 50265 - Training data: EMBO/sd-nlp - Dataset configuration: NER - Training with 48771 examples. - Evaluating on 13801 examples. - Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY - Epochs: 0.6 - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 0.0001 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 ## Eval results Testing on 7178 examples of test set with `sklearn.metrics`: ``` precision recall f1-score support CELL 0.69 0.81 0.74 5245 EXP_ASSAY 0.56 0.57 0.56 10067 GENEPROD 0.77 0.89 0.82 23587 ORGANISM 0.72 0.82 0.77 3623 SMALL_MOLECULE 0.70 0.80 0.75 6187 SUBCELLULAR 0.65 0.72 0.69 3700 TISSUE 0.62 0.73 0.67 3207 micro avg 0.70 0.79 0.74 55616 macro avg 0.67 0.77 0.72 55616 weighted avg 0.70 0.79 0.74 55616 {'test_loss': 0.1830928772687912, 'test_accuracy_score': 0.9334821000160841, 'test_precision': 0.6987463009514112, 'test_recall': 0.789682825086306, 'test_f1': 0.7414366506288511, 'test_runtime': 61.0547, 'test_samples_per_second': 117.567, 'test_steps_per_second': 1.851} ```

{"language": ["english"], "license": "agpl-3.0", "tags": ["token classification"], "datasets": ["EMBO/sd-nlp"], "metrics": []}

EMBO/sd-ner

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #roberta #token-classification #token classification #dataset-EMBO/sd-nlp #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us

# sd-ner ## Model description This model is a RoBERTa base model that was further trained using a masked language modeling task on a compendium of English scientific textual examples from the life sciences using the BioLang dataset. It was then fine-tuned for token classification on the SourceData sd-nlp dataset with the 'NER' configuration to perform Named Entity Recognition of bioentities. ## Intended uses & limitations #### How to use The intended use of this model is for Named Entity Recognition of biological entities used in SourceData annotations (URL), including small molecules, gene products (genes and proteins), subcellular components, cell line and cell types, organ and tissues, species as well as experimental methods. To have a quick check of the model: #### Limitations and bias The model must be used with the 'roberta-base' tokenizer. ## Training data The model was trained for token classification using the EMBO/sd-nlp dataset dataset which includes manually annotated examples. ## Training procedure The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs. Training code is available at URL - Model fine-tuned: EMBO/bio-lm - Tokenizer vocab size: 50265 - Training data: EMBO/sd-nlp - Dataset configuration: NER - Training with 48771 examples. - Evaluating on 13801 examples. - Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY - Epochs: 0.6 - 'per_device_train_batch_size': 16 - 'per_device_eval_batch_size': 16 - 'learning_rate': 0.0001 - 'weight_decay': 0.0 - 'adam_beta1': 0.9 - 'adam_beta2': 0.999 - 'adam_epsilon': 1e-08 - 'max_grad_norm': 1.0 ## Eval results Testing on 7178 examples of test set with 'sklearn.metrics':

token-classification

transformers

# sd-panelization ## Model description This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang). It was then fine-tuned for token classification on the SourceData [sd-nlp](https://huggingface.co/datasets/EMBO/sd-nlp) dataset with the `PANELIZATION` task to perform 'parsing' or 'segmentation' of figure legends into fragments corresponding to sub-panels. Figures are usually composite representations of results obtained with heterogeneous experimental approaches and systems. Breaking figures into panels allows identifying more coherent descriptions of individual scientific experiments. ## Intended uses & limitations #### How to use The intended use of this model is for 'parsing' figure legends into sub-fragments corresponding to individual panels as used in SourceData annotations (https://sourcedata.embo.org). To have a quick check of the model: ```python from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification example = """Fig 4. a, Volume density of early (Avi) and late (Avd) autophagic vacuoles.a, Volume density of early (Avi) and late (Avd) autophagic vacuoles from four independent cultures. Examples of Avi and Avd are shown in b and c, respectively. Bars represent 0.4����m. d, Labelling density of cathepsin-D as estimated in two independent experiments. e, Labelling density of LAMP-1.""" tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512) model = RobertaForTokenClassification.from_pretrained('EMBO/sd-panelization') ner = pipeline('ner', model, tokenizer=tokenizer) res = ner(example) for r in res: print(r['word'], r['entity']) ``` #### Limitations and bias The model must be used with the `roberta-base` tokenizer. ## Training data The model was trained for token classification using the [`EMBO/sd-nlp PANELIZATION`](https://huggingface.co/datasets/EMBO/sd-nlp) dataset which includes manually annotated examples. ## Training procedure The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs. Training code is available at https://github.com/source-data/soda-roberta - Model fine-tuned: EMBO/bio-lm - Tokenizer vocab size: 50265 - Training data: EMBO/sd-nlp - Dataset configuration: PANELIZATION - TTraining with 2175 examples. - Evaluating on 622 examples. - Training on 2 features: `O`, `B-PANEL_START` - Epochs: 1.3 - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 16 - `learning_rate`: 0.0001 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 ## Eval results Testing on 1802 examples from test set with `sklearn.metrics`: ``` precision recall f1-score support PANEL_START 0.89 0.95 0.92 5427 micro avg 0.89 0.95 0.92 5427 macro avg 0.89 0.95 0.92 5427 weighted avg 0.89 0.95 0.92 5427 ```

{"language": ["english"], "license": "agpl-3.0", "tags": ["token classification"], "datasets": ["EMBO/sd-nlp"], "metrics": []}

EMBO/sd-panelization

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #jax #roberta #token-classification #dataset-EMBO/sd-nlp #license-agpl-3.0 #autotrain_compatible #endpoints_compatible #region-us

# sd-panelization ## Model description This model is a RoBERTa base model that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the BioLang dataset. It was then fine-tuned for token classification on the SourceData sd-nlp dataset with the 'PANELIZATION' task to perform 'parsing' or 'segmentation' of figure legends into fragments corresponding to sub-panels. Figures are usually composite representations of results obtained with heterogeneous experimental approaches and systems. Breaking figures into panels allows identifying more coherent descriptions of individual scientific experiments. ## Intended uses & limitations #### How to use The intended use of this model is for 'parsing' figure legends into sub-fragments corresponding to individual panels as used in SourceData annotations (URL). To have a quick check of the model: #### Limitations and bias The model must be used with the 'roberta-base' tokenizer. ## Training data The model was trained for token classification using the 'EMBO/sd-nlp PANELIZATION' dataset which includes manually annotated examples. ## Training procedure The training was run on an NVIDIA DGX Station with 4XTesla V100 GPUs. Training code is available at URL - Model fine-tuned: EMBO/bio-lm - Tokenizer vocab size: 50265 - Training data: EMBO/sd-nlp - Dataset configuration: PANELIZATION - TTraining with 2175 examples. - Evaluating on 622 examples. - Training on 2 features: 'O', 'B-PANEL_START' - Epochs: 1.3 - 'per_device_train_batch_size': 16 - 'per_device_eval_batch_size': 16 - 'learning_rate': 0.0001 - 'weight_decay': 0.0 - 'adam_beta1': 0.9 - 'adam_beta2': 0.999 - 'adam_epsilon': 1e-08 - 'max_grad_norm': 1.0 ## Eval results Testing on 1802 examples from test set with 'sklearn.metrics':

text-generation

transformers

# Game of Thrones DialoGPT Model

{"tags": ["conversational"]}

ESPersonnel/DialoGPT-small-got

2022-03-02T23:29:04+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Game of Thrones DialoGPT Model