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--- |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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base_model: facebook/mms-1b-all |
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tags: |
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- automatic-speech-recognition |
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- natbed |
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- mms |
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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: mms-1b-all-bem-natbed-nn-model |
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results: [] |
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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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# mms-1b-all-bem-natbed-nn-model |
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the NATBED - BEM dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5884 |
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- Wer: 0.5333 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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: 100 |
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- num_epochs: 30.0 |
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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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| 7.9244 | 0.2257 | 100 | 1.3514 | 1.0238 | |
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| 1.0236 | 0.4515 | 200 | 0.8355 | 0.6595 | |
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| 0.8005 | 0.6772 | 300 | 0.7837 | 0.6141 | |
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| 0.8968 | 0.9029 | 400 | 0.7809 | 0.6043 | |
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| 0.8909 | 1.1287 | 500 | 0.7147 | 0.5953 | |
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| 0.7983 | 1.3544 | 600 | 0.6990 | 0.5931 | |
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| 0.8563 | 1.5801 | 700 | 0.6805 | 0.5965 | |
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| 0.7094 | 1.8059 | 800 | 0.6849 | 0.5808 | |
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| 0.7499 | 2.0316 | 900 | 0.6457 | 0.5934 | |
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| 0.7722 | 2.2573 | 1000 | 0.6565 | 0.5875 | |
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| 0.7099 | 2.4831 | 1100 | 0.6419 | 0.5596 | |
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| 0.7416 | 2.7088 | 1200 | 0.6195 | 0.5611 | |
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| 0.6385 | 2.9345 | 1300 | 0.6228 | 0.5647 | |
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| 0.6436 | 3.1603 | 1400 | 0.6184 | 0.5510 | |
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| 0.6795 | 3.3860 | 1500 | 0.6157 | 0.5533 | |
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| 0.7027 | 3.6117 | 1600 | 0.6343 | 0.5426 | |
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| 0.6585 | 3.8375 | 1700 | 0.6057 | 0.5428 | |
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| 0.6351 | 4.0632 | 1800 | 0.6017 | 0.5430 | |
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| 0.6528 | 4.2889 | 1900 | 0.6099 | 0.5340 | |
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| 0.6603 | 4.5147 | 2000 | 0.6218 | 0.5335 | |
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| 0.6676 | 4.7404 | 2100 | 0.5977 | 0.5323 | |
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| 0.6304 | 4.9661 | 2200 | 0.5884 | 0.5333 | |
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| 0.5976 | 5.1919 | 2300 | 0.5956 | 0.5228 | |
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| 0.6564 | 5.4176 | 2400 | 0.5957 | 0.5302 | |
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| 0.6717 | 5.6433 | 2500 | 0.5767 | 0.5183 | |
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| 0.6091 | 5.8691 | 2600 | 0.5921 | 0.5273 | |
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| 0.6168 | 6.0948 | 2700 | 0.5894 | 0.5275 | |
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| 0.6495 | 6.3205 | 2800 | 0.6036 | 0.5197 | |
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### Framework versions |
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- Transformers 4.46.0.dev0 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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