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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-tamil_new |
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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-tamil_new |
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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: 0.0960 |
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- Wer: 0.1781 |
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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: 4e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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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: 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: 2000 |
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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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| 0.3099 | 0.1547 | 2000 | 0.2685 | 0.4726 | |
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| 0.2319 | 0.3094 | 4000 | 0.2052 | 0.3246 | |
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| 0.21 | 0.4640 | 6000 | 0.1702 | 0.2968 | |
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| 0.1907 | 0.6187 | 8000 | 0.1591 | 0.2809 | |
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| 0.1789 | 0.7734 | 10000 | 0.1468 | 0.2703 | |
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| 0.1626 | 0.9281 | 12000 | 0.1482 | 0.2540 | |
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| 0.1469 | 1.0828 | 14000 | 0.1390 | 0.2536 | |
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| 0.144 | 1.2375 | 16000 | 0.1298 | 0.2433 | |
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| 0.1418 | 1.3921 | 18000 | 0.1287 | 0.2399 | |
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| 0.1349 | 1.5468 | 20000 | 0.1219 | 0.2343 | |
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| 0.1266 | 1.7015 | 22000 | 0.1229 | 0.2349 | |
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| 0.1257 | 1.8562 | 24000 | 0.1202 | 0.2241 | |
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| 0.1209 | 2.0109 | 26000 | 0.1193 | 0.2176 | |
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| 0.1113 | 2.1655 | 28000 | 0.1146 | 0.2150 | |
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| 0.1052 | 2.3202 | 30000 | 0.1165 | 0.2234 | |
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| 0.103 | 2.4749 | 32000 | 0.1130 | 0.2112 | |
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| 0.0988 | 2.6296 | 34000 | 0.1092 | 0.2029 | |
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| 0.098 | 2.7843 | 36000 | 0.1061 | 0.2022 | |
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| 0.1007 | 2.9390 | 38000 | 0.1054 | 0.2036 | |
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| 0.0823 | 3.0936 | 40000 | 0.1042 | 0.1997 | |
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| 0.0866 | 3.2483 | 42000 | 0.1020 | 0.1945 | |
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| 0.0874 | 3.4030 | 44000 | 0.0993 | 0.1972 | |
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| 0.0825 | 3.5577 | 46000 | 0.1012 | 0.1941 | |
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| 0.083 | 3.7124 | 48000 | 0.1017 | 0.1911 | |
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| 0.0724 | 3.8671 | 50000 | 0.0992 | 0.1904 | |
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| 0.0761 | 4.0217 | 52000 | 0.0983 | 0.1856 | |
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| 0.0641 | 4.1764 | 54000 | 0.1011 | 0.1857 | |
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| 0.0611 | 4.3311 | 56000 | 0.0980 | 0.1821 | |
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| 0.0646 | 4.4858 | 58000 | 0.0982 | 0.1816 | |
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| 0.062 | 4.6405 | 60000 | 0.0962 | 0.1786 | |
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| 0.0616 | 4.7951 | 62000 | 0.0951 | 0.1787 | |
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| 0.0607 | 4.9498 | 64000 | 0.0960 | 0.1781 | |
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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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