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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-odia_v1 |
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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-odia_v1 |
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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: 0.0767 |
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- Wer: 0.1256 |
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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: 3.5356e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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: 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 | |
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|:-------------:|:------:|:----:|:---------------:|:------:| |
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| 0.4216 | 0.3733 | 300 | 0.2149 | 0.3309 | |
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| 0.2996 | 0.7465 | 600 | 0.1719 | 0.2572 | |
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| 0.2271 | 1.1198 | 900 | 0.1366 | 0.2390 | |
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| 0.1917 | 1.4930 | 1200 | 0.1137 | 0.2054 | |
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| 0.167 | 1.8663 | 1500 | 0.1208 | 0.2046 | |
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| 0.1371 | 2.2395 | 1800 | 0.0995 | 0.1995 | |
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| 0.133 | 2.6128 | 2100 | 0.1006 | 0.1944 | |
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| 0.1214 | 2.9860 | 2400 | 0.0958 | 0.1715 | |
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| 0.101 | 3.3593 | 2700 | 0.0853 | 0.1602 | |
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| 0.1007 | 3.7325 | 3000 | 0.0851 | 0.1667 | |
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| 0.0898 | 4.1058 | 3300 | 0.0820 | 0.1532 | |
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| 0.089 | 4.4790 | 3600 | 0.0814 | 0.1539 | |
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| 0.0776 | 4.8523 | 3900 | 0.0792 | 0.1479 | |
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| 0.0655 | 5.2255 | 4200 | 0.0782 | 0.1438 | |
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| 0.0708 | 5.5988 | 4500 | 0.0770 | 0.1391 | |
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| 0.0662 | 5.9720 | 4800 | 0.0727 | 0.1372 | |
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| 0.0556 | 6.3453 | 5100 | 0.0757 | 0.1372 | |
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| 0.0629 | 6.7185 | 5400 | 0.0729 | 0.1319 | |
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| 0.0472 | 7.0918 | 5700 | 0.0771 | 0.1369 | |
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| 0.0546 | 7.4650 | 6000 | 0.0760 | 0.1378 | |
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| 0.041 | 7.8383 | 6300 | 0.0750 | 0.1402 | |
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| 0.0405 | 8.2115 | 6600 | 0.0776 | 0.1340 | |
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| 0.0395 | 8.5848 | 6900 | 0.0741 | 0.1306 | |
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| 0.0366 | 8.9580 | 7200 | 0.0742 | 0.1255 | |
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| 0.0288 | 9.3313 | 7500 | 0.0767 | 0.1296 | |
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| 0.0329 | 9.7045 | 7800 | 0.0767 | 0.1256 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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