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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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metrics: |
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- wer |
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model-index: |
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- name: w2v-bert-2.0-malayalam_mixeddataset_two.0 |
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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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# w2v-bert-2.0-malayalam_mixeddataset_two.0 |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1425 |
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- Wer: 0.1451 |
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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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- 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: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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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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| 1.9341 | 0.24 | 300 | 0.4363 | 0.5138 | |
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| 0.228 | 0.47 | 600 | 0.3644 | 0.4847 | |
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| 0.1828 | 0.71 | 900 | 0.2752 | 0.3807 | |
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| 0.1479 | 0.95 | 1200 | 0.2671 | 0.3583 | |
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| 0.1213 | 1.19 | 1500 | 0.2291 | 0.2861 | |
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| 0.1114 | 1.42 | 1800 | 0.2098 | 0.2754 | |
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| 0.1049 | 1.66 | 2100 | 0.2088 | 0.2832 | |
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| 0.0962 | 1.9 | 2400 | 0.1789 | 0.2501 | |
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| 0.0777 | 2.14 | 2700 | 0.1945 | 0.2371 | |
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| 0.0685 | 2.37 | 3000 | 0.1788 | 0.2433 | |
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| 0.0663 | 2.61 | 3300 | 0.1707 | 0.2264 | |
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| 0.0652 | 2.85 | 3600 | 0.1834 | 0.2227 | |
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| 0.0573 | 3.08 | 3900 | 0.1663 | 0.2065 | |
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| 0.0445 | 3.32 | 4200 | 0.1479 | 0.1981 | |
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| 0.0417 | 3.56 | 4500 | 0.1477 | 0.1779 | |
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| 0.0415 | 3.8 | 4800 | 0.1504 | 0.1774 | |
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| 0.0368 | 4.03 | 5100 | 0.1407 | 0.1655 | |
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| 0.0248 | 4.27 | 5400 | 0.1568 | 0.1672 | |
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| 0.0258 | 4.51 | 5700 | 0.1495 | 0.1582 | |
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| 0.0227 | 4.74 | 6000 | 0.1460 | 0.1510 | |
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| 0.0225 | 4.98 | 6300 | 0.1425 | 0.1451 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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