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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-Adapter-Testing |
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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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[<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/tamimhasanbhuiyan/huggingface/runs/qiuhht9t) |
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# MMS-Adapter-Testing |
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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: 0.9386 |
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- Wer: 0.6378 |
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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: 0.001 |
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- train_batch_size: 2 |
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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: 10 |
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- num_epochs: 1 |
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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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| 5.768 | 0.0150 | 100 | 2.1552 | 0.9390 | |
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| 2.3489 | 0.0299 | 200 | 1.1714 | 0.7209 | |
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| 1.7924 | 0.0449 | 300 | 1.1720 | 0.7586 | |
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| 1.7483 | 0.0598 | 400 | 1.0868 | 0.7237 | |
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| 1.8404 | 0.0748 | 500 | 1.0824 | 0.6963 | |
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| 1.8122 | 0.0897 | 600 | 1.0771 | 0.6866 | |
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| 1.7504 | 0.1047 | 700 | 1.0705 | 0.6970 | |
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| 1.6675 | 0.1196 | 800 | 1.0688 | 0.6913 | |
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| 1.6123 | 0.1346 | 900 | 1.0446 | 0.6888 | |
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| 1.6237 | 0.1495 | 1000 | 1.0586 | 0.7034 | |
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| 1.6714 | 0.1645 | 1100 | 1.0562 | 0.6866 | |
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| 1.8129 | 0.1795 | 1200 | 1.0363 | 0.6891 | |
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| 1.7839 | 0.1944 | 1300 | 1.0374 | 0.6631 | |
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| 1.7305 | 0.2094 | 1400 | 1.0211 | 0.6834 | |
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| 1.5496 | 0.2243 | 1500 | 1.0225 | 0.6856 | |
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| 1.5106 | 0.2393 | 1600 | 1.0387 | 0.7127 | |
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| 1.7517 | 0.2542 | 1700 | 1.0561 | 0.6898 | |
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| 1.7117 | 0.2692 | 1800 | 1.0303 | 0.6866 | |
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| 1.6854 | 0.2841 | 1900 | 1.0240 | 0.6888 | |
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| 1.5186 | 0.2991 | 2000 | 1.0207 | 0.6873 | |
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| 1.5631 | 0.3140 | 2100 | 0.9964 | 0.6677 | |
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| 1.6909 | 0.3290 | 2200 | 1.0090 | 0.6738 | |
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| 1.5698 | 0.3440 | 2300 | 1.0016 | 0.6809 | |
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| 1.6702 | 0.3589 | 2400 | 0.9996 | 0.6749 | |
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| 1.628 | 0.3739 | 2500 | 1.0074 | 0.6699 | |
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| 1.8025 | 0.3888 | 2600 | 1.0312 | 0.6934 | |
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| 1.5986 | 0.4038 | 2700 | 0.9871 | 0.6667 | |
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| 1.5687 | 0.4187 | 2800 | 0.9893 | 0.6567 | |
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| 1.6444 | 0.4337 | 2900 | 0.9943 | 0.6674 | |
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| 1.5869 | 0.4486 | 3000 | 0.9831 | 0.6706 | |
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| 1.443 | 0.4636 | 3100 | 1.0192 | 0.7045 | |
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| 1.569 | 0.4785 | 3200 | 0.9783 | 0.6635 | |
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| 1.5302 | 0.4935 | 3300 | 0.9898 | 0.6727 | |
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| 1.5879 | 0.5084 | 3400 | 0.9773 | 0.6670 | |
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| 1.5739 | 0.5234 | 3500 | 0.9837 | 0.6895 | |
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| 1.5684 | 0.5384 | 3600 | 0.9836 | 0.6667 | |
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| 1.6397 | 0.5533 | 3700 | 0.9673 | 0.6578 | |
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| 1.5639 | 0.5683 | 3800 | 0.9888 | 0.6599 | |
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| 1.6773 | 0.5832 | 3900 | 0.9788 | 0.6613 | |
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| 1.5069 | 0.5982 | 4000 | 0.9801 | 0.6542 | |
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| 1.4801 | 0.6131 | 4100 | 0.9587 | 0.6545 | |
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| 1.7308 | 0.6281 | 4200 | 0.9599 | 0.6706 | |
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| 1.4852 | 0.6430 | 4300 | 0.9728 | 0.6663 | |
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| 1.4654 | 0.6580 | 4400 | 0.9468 | 0.6417 | |
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| 1.801 | 0.6729 | 4500 | 0.9591 | 0.6556 | |
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| 2.0928 | 0.6879 | 4600 | 0.9857 | 0.6670 | |
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| 1.561 | 0.7029 | 4700 | 0.9550 | 0.6503 | |
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| 1.6623 | 0.7178 | 4800 | 0.9587 | 0.6524 | |
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| 1.5252 | 0.7328 | 4900 | 0.9551 | 0.6531 | |
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| 1.5539 | 0.7477 | 5000 | 0.9660 | 0.6513 | |
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| 1.5571 | 0.7627 | 5100 | 0.9557 | 0.6531 | |
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| 1.6584 | 0.7776 | 5200 | 0.9649 | 0.6563 | |
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| 1.5072 | 0.7926 | 5300 | 0.9604 | 0.6481 | |
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| 1.5362 | 0.8075 | 5400 | 0.9457 | 0.6314 | |
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| 1.4772 | 0.8225 | 5500 | 0.9491 | 0.6449 | |
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| 1.3731 | 0.8374 | 5600 | 0.9609 | 0.6478 | |
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| 1.5795 | 0.8524 | 5700 | 0.9568 | 0.6567 | |
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| 1.4013 | 0.8674 | 5800 | 0.9457 | 0.6406 | |
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| 1.5817 | 0.8823 | 5900 | 0.9437 | 0.6513 | |
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| 1.4211 | 0.8973 | 6000 | 0.9433 | 0.6381 | |
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| 1.4341 | 0.9122 | 6100 | 0.9420 | 0.6353 | |
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| 1.4818 | 0.9272 | 6200 | 0.9407 | 0.6456 | |
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| 1.5241 | 0.9421 | 6300 | 0.9400 | 0.6381 | |
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| 1.575 | 0.9571 | 6400 | 0.9374 | 0.6392 | |
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| 1.5232 | 0.9720 | 6500 | 0.9385 | 0.6364 | |
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| 1.8634 | 0.9870 | 6600 | 0.9386 | 0.6378 | |
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### Framework versions |
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- Transformers 4.41.0.dev0 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.19.1 |
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