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--- |
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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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- 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-MGB3 |
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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-MGB3 |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.5770 |
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- Wer: 99.9986 |
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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: 1e-05 |
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- train_batch_size: 14 |
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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: constant_with_warmup |
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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 25 |
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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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| 8.1649 | 0.83 | 250 | 8.8182 | 100.0211 | |
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| 4.4764 | 1.66 | 500 | 5.1784 | 100.0254 | |
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| 3.962 | 2.48 | 750 | 4.6853 | 100.0310 | |
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| 3.7546 | 3.31 | 1000 | 4.4820 | 101.1220 | |
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| 3.5712 | 4.14 | 1250 | 4.3419 | 101.5181 | |
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| 5.9242 | 4.97 | 1500 | 4.2276 | 100.0 | |
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| 6.072 | 5.79 | 1750 | 4.1441 | 100.0 | |
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| 3.3164 | 6.62 | 2000 | 4.0701 | 100.0 | |
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| 3.2964 | 7.45 | 2250 | 3.9941 | 100.0 | |
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| 3.2501 | 8.28 | 2500 | 3.9978 | 100.0 | |
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| 3.2477 | 9.11 | 2750 | 3.9468 | 100.0 | |
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| 3.9197 | 9.93 | 3000 | 3.9109 | 100.0 | |
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| 3.1928 | 10.76 | 3250 | 3.8802 | 100.0 | |
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| 3.182 | 11.59 | 3500 | 3.8802 | 100.0 | |
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| 3.181 | 12.42 | 3750 | 3.7959 | 100.0 | |
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| 4.3975 | 13.25 | 4000 | 3.8292 | 100.0 | |
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| 3.8885 | 14.07 | 4250 | 3.7765 | 100.0 | |
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| 4.2643 | 14.9 | 4500 | 3.7765 | 100.0 | |
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| 3.1381 | 15.73 | 4750 | 3.7338 | 100.0 | |
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| 3.1197 | 16.56 | 5000 | 3.7391 | 100.0 | |
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| 3.1345 | 17.38 | 5250 | 3.7267 | 100.0 | |
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| 4.4275 | 18.21 | 5500 | 3.7405 | 100.0 | |
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| 4.3669 | 19.04 | 5750 | 3.7220 | 100.0 | |
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| 3.1225 | 19.87 | 6000 | 3.7093 | 99.9915 | |
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| 3.8043 | 20.7 | 6250 | 3.6449 | 99.9958 | |
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| 3.8089 | 21.52 | 6500 | 3.6988 | 100.0 | |
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| 4.2457 | 22.35 | 6750 | 3.6125 | 100.0 | |
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| 3.0956 | 23.18 | 7000 | 3.6309 | 99.9972 | |
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| 3.1013 | 24.01 | 7250 | 3.5845 | 99.9930 | |
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| 5.8493 | 24.83 | 7500 | 3.5770 | 99.9986 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.19.1 |
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- Tokenizers 0.13.3 |
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