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library_name: transformers
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---
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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---
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library_name: transformers
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license: apache-2.0
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base_model: facebook/wav2vec2-large-xlsr-53
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tags:
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- generated_from_trainer
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metrics:
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- wer
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model-index:
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- name: wav2vec2-large-xlsr-faroese-100h-30k-steps
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results: []
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# wav2vec2-large-xlsr-faroese-100h-30k-steps
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1354
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- Wer: 25.2668
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0003
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 500
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- training_steps: 30000
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-------:|:-----:|:---------------:|:-------:|
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| 0.6576 | 0.4640 | 1000 | 0.4914 | 63.2323 |
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| 0.4625 | 0.9281 | 2000 | 0.3354 | 47.7149 |
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| 0.3475 | 1.3921 | 3000 | 0.2567 | 41.2321 |
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| 0.2979 | 1.8561 | 4000 | 0.2330 | 38.5595 |
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| 0.235 | 2.3202 | 5000 | 0.2173 | 37.0143 |
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| 0.2737 | 2.7842 | 6000 | 0.2089 | 35.6704 |
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| 0.2095 | 3.2483 | 7000 | 0.1939 | 33.8484 |
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| 0.1916 | 3.7123 | 8000 | 0.1836 | 33.3904 |
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| 0.176 | 4.1763 | 9000 | 0.1794 | 31.9609 |
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| 0.1609 | 4.6404 | 10000 | 0.1709 | 31.3771 |
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| 0.1941 | 5.1044 | 11000 | 0.1693 | 30.9392 |
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| 0.1517 | 5.5684 | 12000 | 0.1693 | 30.9493 |
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| 0.1583 | 6.0325 | 13000 | 0.1532 | 29.6859 |
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| 0.14 | 6.4965 | 14000 | 0.1604 | 29.3185 |
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| 0.1688 | 6.9606 | 15000 | 0.1488 | 29.4041 |
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| 0.1553 | 7.4246 | 16000 | 0.1607 | 28.6893 |
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| 0.1483 | 7.8886 | 17000 | 0.1526 | 28.0552 |
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| 0.1442 | 8.3527 | 18000 | 0.1537 | 28.2615 |
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| 0.1304 | 8.8167 | 19000 | 0.1497 | 27.5569 |
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| 0.1104 | 9.2807 | 20000 | 0.1622 | 27.5871 |
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| 0.1225 | 9.7448 | 21000 | 0.1493 | 26.8724 |
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| 0.1014 | 10.2088 | 22000 | 0.1433 | 26.7516 |
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| 0.1087 | 10.6729 | 23000 | 0.1365 | 26.2130 |
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| 0.0855 | 11.1369 | 24000 | 0.1421 | 26.2432 |
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| 0.0865 | 11.6009 | 25000 | 0.1339 | 25.9714 |
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| 0.0603 | 12.0650 | 26000 | 0.1364 | 25.6694 |
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| 0.0663 | 12.5290 | 27000 | 0.1362 | 25.3876 |
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| 0.0648 | 12.9930 | 28000 | 0.1358 | 25.4681 |
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| 0.0638 | 13.4571 | 29000 | 0.1366 | 25.3423 |
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| 0.0629 | 13.9211 | 30000 | 0.1354 | 25.2668 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.4.1+cu121
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- Datasets 3.0.0
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- Tokenizers 0.19.1
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