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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-malayalam |
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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-malayalam |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1149 |
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- Wer: 0.0646 |
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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 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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- 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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| 0.3705 | 0.2758 | 2000 | 0.3227 | 0.3629 | |
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| 0.291 | 0.5516 | 4000 | 0.2434 | 0.2891 | |
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| 0.2695 | 0.8274 | 6000 | 0.2445 | 0.2775 | |
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| 0.2118 | 1.1032 | 8000 | 0.1979 | 0.2567 | |
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| 0.1923 | 1.3790 | 10000 | 0.1852 | 0.2213 | |
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| 0.1788 | 1.6548 | 12000 | 0.1691 | 0.2033 | |
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| 0.167 | 1.9306 | 14000 | 0.1870 | 0.1955 | |
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| 0.1612 | 2.2063 | 16000 | 0.1571 | 0.1731 | |
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| 0.1516 | 2.4821 | 18000 | 0.1406 | 0.1685 | |
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| 0.1597 | 2.7579 | 20000 | 0.1358 | 0.1496 | |
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| 0.1299 | 3.0336 | 22000 | 0.1332 | 0.1397 | |
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| 0.1096 | 3.3095 | 24000 | 0.1397 | 0.1384 | |
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| 0.1291 | 3.5853 | 26000 | 0.1298 | 0.1354 | |
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| 0.0975 | 3.8611 | 28000 | 0.1220 | 0.1134 | |
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| 0.0919 | 4.1368 | 30000 | 0.1261 | 0.1081 | |
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| 0.0806 | 4.4126 | 32000 | 0.1189 | 0.1120 | |
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| 0.0778 | 4.6884 | 34000 | 0.1159 | 0.1027 | |
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| 0.0922 | 4.9642 | 36000 | 0.1218 | 0.1027 | |
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| 0.0907 | 5.2400 | 38000 | 0.1099 | 0.0977 | |
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| 0.0708 | 5.5158 | 40000 | 0.1043 | 0.0920 | |
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| 0.0715 | 5.7916 | 42000 | 0.1048 | 0.0928 | |
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| 0.0646 | 6.0673 | 44000 | 0.1047 | 0.0893 | |
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| 0.0567 | 6.3431 | 46000 | 0.1294 | 0.0891 | |
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| 0.0729 | 6.6189 | 48000 | 0.1236 | 0.0873 | |
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| 0.0607 | 6.8947 | 50000 | 0.1182 | 0.0830 | |
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| 0.0555 | 7.1705 | 52000 | 0.1222 | 0.0809 | |
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| 0.0516 | 7.4463 | 54000 | 0.1145 | 0.0798 | |
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| 0.0429 | 7.7221 | 56000 | 0.0915 | 0.0763 | |
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| 0.0399 | 7.9979 | 58000 | 0.0987 | 0.0731 | |
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| 0.0373 | 8.2736 | 60000 | 0.1167 | 0.0714 | |
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| 0.0371 | 8.5494 | 62000 | 0.1130 | 0.0710 | |
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| 0.0412 | 8.8252 | 64000 | 0.1194 | 0.0707 | |
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| 0.0282 | 9.1009 | 66000 | 0.1217 | 0.0683 | |
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| 0.0284 | 9.3768 | 68000 | 0.1177 | 0.0671 | |
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| 0.0275 | 9.6526 | 70000 | 0.1117 | 0.0661 | |
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| 0.0216 | 9.9284 | 72000 | 0.1149 | 0.0646 | |
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### Framework versions |
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- Transformers 4.48.0 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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