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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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base_model: facebook/wav2vec2-xls-r-300m |
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datasets: |
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- common_voice_17_0 |
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metrics: |
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- wer |
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model-index: |
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- name: xls-r-300m-hbs-phoneme-unfrozen-batch16 |
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results: |
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- task: |
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type: automatic-speech-recognition |
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name: Automatic Speech Recognition |
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dataset: |
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name: common_voice_17_0 |
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type: common_voice_17_0 |
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config: hsb |
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split: test |
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args: hsb |
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metrics: |
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- type: wer |
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value: 0.4111996251171509 |
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name: Wer |
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--- |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/badr-nlp/xlsr-continual-finetuning/runs/7invqf4p) |
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# xls-r-300m-hbs-phoneme-unfrozen-batch16 |
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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_17_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7105 |
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- Wer: 0.4112 |
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- Cer: 0.0948 |
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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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- num_epochs: 100 |
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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 | Cer | |
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|:-------------:|:-------:|:----:|:---------------:|:------:|:------:| |
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| 3.6184 | 3.2258 | 100 | 3.4215 | 1.0 | 1.0 | |
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| 3.2927 | 6.4516 | 200 | 3.2247 | 1.0 | 1.0 | |
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| 3.2291 | 9.6774 | 300 | 3.2021 | 1.0 | 1.0000 | |
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| 1.4844 | 12.9032 | 400 | 1.3507 | 0.9857 | 0.2837 | |
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| 0.4136 | 16.1290 | 500 | 0.6982 | 0.6567 | 0.1608 | |
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| 0.2346 | 19.3548 | 600 | 0.6496 | 0.5956 | 0.1466 | |
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| 0.1401 | 22.5806 | 700 | 0.6680 | 0.5565 | 0.1314 | |
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| 0.1535 | 25.8065 | 800 | 0.6597 | 0.5026 | 0.1190 | |
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| 0.1165 | 29.0323 | 900 | 0.7085 | 0.5112 | 0.1224 | |
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| 0.076 | 32.2581 | 1000 | 0.7359 | 0.5026 | 0.1195 | |
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| 0.083 | 35.4839 | 1100 | 0.7144 | 0.4991 | 0.1205 | |
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| 0.0985 | 38.7097 | 1200 | 0.6907 | 0.4756 | 0.1120 | |
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| 0.052 | 41.9355 | 1300 | 0.6806 | 0.4700 | 0.1105 | |
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| 0.0347 | 45.1613 | 1400 | 0.7097 | 0.4588 | 0.1091 | |
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| 0.0432 | 48.3871 | 1500 | 0.7086 | 0.4649 | 0.1093 | |
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| 0.0626 | 51.6129 | 1600 | 0.6947 | 0.4393 | 0.1029 | |
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| 0.0474 | 54.8387 | 1700 | 0.6915 | 0.4468 | 0.1058 | |
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| 0.057 | 58.0645 | 1800 | 0.7068 | 0.4358 | 0.1020 | |
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| 0.0373 | 61.2903 | 1900 | 0.7140 | 0.4419 | 0.1037 | |
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| 0.0994 | 64.5161 | 2000 | 0.6966 | 0.4208 | 0.0987 | |
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| 0.0503 | 67.7419 | 2100 | 0.6997 | 0.4306 | 0.0988 | |
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| 0.0418 | 70.9677 | 2200 | 0.7105 | 0.4353 | 0.1006 | |
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| 0.036 | 74.1935 | 2300 | 0.7320 | 0.4356 | 0.1024 | |
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| 0.0171 | 77.4194 | 2400 | 0.7132 | 0.4257 | 0.0994 | |
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| 0.0234 | 80.6452 | 2500 | 0.7059 | 0.4171 | 0.0967 | |
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| 0.0335 | 83.8710 | 2600 | 0.7449 | 0.4140 | 0.0973 | |
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| 0.0288 | 87.0968 | 2700 | 0.7028 | 0.4157 | 0.0964 | |
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| 0.0344 | 90.3226 | 2800 | 0.7181 | 0.4112 | 0.0960 | |
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| 0.0298 | 93.5484 | 2900 | 0.7150 | 0.4105 | 0.0951 | |
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| 0.0532 | 96.7742 | 3000 | 0.7164 | 0.4119 | 0.0950 | |
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| 0.0058 | 100.0 | 3100 | 0.7105 | 0.4112 | 0.0948 | |
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### Framework versions |
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- Transformers 4.42.0.dev0 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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