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--- |
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license: mit |
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base_model: facebook/w2v-bert-2.0 |
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tags: |
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- generated_from_trainer |
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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: adapter_freezed_base_const_lr |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: 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: hy-AM |
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split: test |
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args: hy-AM |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.9281584969288209 |
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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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# adapter_freezed_base_const_lr |
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This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) 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: 0.9200 |
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- Wer: 0.9282 |
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- Cer: 0.2562 |
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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: 0.0001 |
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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: constant |
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- num_epochs: 10 |
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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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| 1.3224 | 0.6154 | 200 | 1.3171 | 0.9949 | 0.3890 | |
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| 1.02 | 1.2308 | 400 | 1.0780 | 0.9728 | 0.3233 | |
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| 0.9256 | 1.8462 | 600 | 0.9799 | 0.9738 | 0.2955 | |
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| 0.8377 | 2.4615 | 800 | 0.9756 | 0.9663 | 0.2919 | |
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| 0.7836 | 3.0769 | 1000 | 0.9143 | 0.9535 | 0.2730 | |
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| 0.7516 | 3.6923 | 1200 | 0.8908 | 0.9373 | 0.2671 | |
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| 0.6714 | 4.3077 | 1400 | 0.9088 | 0.9497 | 0.2692 | |
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| 0.6749 | 4.9231 | 1600 | 0.9006 | 0.9566 | 0.2681 | |
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| 0.6223 | 5.5385 | 1800 | 0.8686 | 0.9322 | 0.2587 | |
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| 0.5643 | 6.1538 | 2000 | 0.8846 | 0.9422 | 0.2580 | |
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| 0.5773 | 6.7692 | 2200 | 0.8960 | 0.9396 | 0.2644 | |
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| 0.5067 | 7.3846 | 2400 | 0.8778 | 0.9273 | 0.2545 | |
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| 0.5123 | 8.0 | 2600 | 0.8919 | 0.9379 | 0.2601 | |
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| 0.4729 | 8.6154 | 2800 | 0.9131 | 0.9597 | 0.2587 | |
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| 0.406 | 9.2308 | 3000 | 0.9032 | 0.9389 | 0.2564 | |
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| 0.4286 | 9.8462 | 3200 | 0.9200 | 0.9282 | 0.2562 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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