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--- |
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base_model: Aviral2412/mini_model |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_1_0 |
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metrics: |
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- wer |
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model-index: |
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- name: fineturning-with-pretraining |
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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_1_0 |
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type: common_voice_1_0 |
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config: en |
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split: validation |
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args: en |
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metrics: |
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- name: Wer |
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type: wer |
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value: 1.0010991853097115 |
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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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# fineturning-with-pretraining |
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This model is a fine-tuned version of [Aviral2412/mini_model](https://huggingface.co/Aviral2412/mini_model) on the common_voice_1_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3739 |
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- Wer: 1.0011 |
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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: 5e-05 |
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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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- 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: 25 |
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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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| 4.3381 | 2.15 | 500 | 2.5389 | 1.0011 | |
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| 2.4622 | 4.29 | 1000 | 2.4761 | 1.0011 | |
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| 2.4477 | 6.44 | 1500 | 2.5567 | 1.0011 | |
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| 2.4325 | 8.58 | 2000 | 2.4334 | 1.0011 | |
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| 2.4205 | 10.73 | 2500 | 2.4067 | 1.0011 | |
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| 2.3995 | 12.88 | 3000 | 2.3828 | 1.0011 | |
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| 2.3869 | 15.02 | 3500 | 2.3752 | 1.0011 | |
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| 2.3857 | 17.17 | 4000 | 2.3759 | 1.0011 | |
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| 2.3717 | 19.31 | 4500 | 2.3684 | 1.0011 | |
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| 2.3625 | 21.46 | 5000 | 2.3601 | 1.0011 | |
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| 2.3648 | 23.61 | 5500 | 2.3739 | 1.0011 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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