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--- |
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library_name: transformers |
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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-hindi_voip |
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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-hindi_voip |
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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: 0.1299 |
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- Wer: 0.0737 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- training_steps: 60000 |
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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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| 0.9895 | 0.1439 | 2000 | 0.8051 | 0.4624 | |
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| 0.8084 | 0.2878 | 4000 | 0.6416 | 0.3877 | |
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| 0.6948 | 0.4317 | 6000 | 0.5950 | 0.3660 | |
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| 0.6599 | 0.5756 | 8000 | 0.5228 | 0.3330 | |
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| 0.564 | 0.7195 | 10000 | 0.4980 | 0.3001 | |
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| 0.5112 | 0.8634 | 12000 | 0.4413 | 0.2928 | |
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| 0.4827 | 1.0073 | 14000 | 0.3999 | 0.2646 | |
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| 0.4198 | 1.1512 | 16000 | 0.3734 | 0.2438 | |
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| 0.3831 | 1.2951 | 18000 | 0.3779 | 0.2365 | |
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| 0.3699 | 1.4390 | 20000 | 0.3223 | 0.2144 | |
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| 0.34 | 1.5828 | 22000 | 0.3074 | 0.1973 | |
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| 0.3157 | 1.7267 | 24000 | 0.2809 | 0.1922 | |
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| 0.3041 | 1.8706 | 26000 | 0.2538 | 0.1751 | |
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| 0.2642 | 2.0145 | 28000 | 0.2620 | 0.1687 | |
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| 0.2296 | 2.1584 | 30000 | 0.2355 | 0.1521 | |
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| 0.2099 | 2.3023 | 32000 | 0.2279 | 0.1454 | |
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| 0.2037 | 2.4462 | 34000 | 0.2235 | 0.1405 | |
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| 0.1905 | 2.5901 | 36000 | 0.1988 | 0.1244 | |
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| 0.1876 | 2.7340 | 38000 | 0.1919 | 0.1227 | |
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| 0.1739 | 2.8779 | 40000 | 0.1865 | 0.1153 | |
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| 0.1387 | 3.0218 | 42000 | 0.1831 | 0.1077 | |
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| 0.1261 | 3.1657 | 44000 | 0.1760 | 0.1007 | |
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| 0.1146 | 3.3096 | 46000 | 0.1597 | 0.0945 | |
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| 0.1142 | 3.4535 | 48000 | 0.1602 | 0.0936 | |
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| 0.0998 | 3.5974 | 50000 | 0.1472 | 0.0861 | |
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| 0.102 | 3.7413 | 52000 | 0.1413 | 0.0823 | |
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| 0.0985 | 3.8852 | 54000 | 0.1376 | 0.0783 | |
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| 0.08 | 4.0291 | 56000 | 0.1338 | 0.0765 | |
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| 0.072 | 4.1730 | 58000 | 0.1319 | 0.0743 | |
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| 0.0677 | 4.3169 | 60000 | 0.1299 | 0.0737 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.7.0+cu126 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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