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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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base_model: facebook/w2v-bert-2.0 |
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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-tamil-gpu-custom_clean_v2 |
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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-tamil-gpu-custom_clean_v2 |
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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: 0.1042 |
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- Wer: 0.1892 |
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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: 2.5356e-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: 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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| 2.9949 | 0.25 | 300 | 0.5158 | 0.6736 | |
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| 0.4051 | 0.5 | 600 | 0.1858 | 0.3035 | |
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| 0.2789 | 0.76 | 900 | 0.1670 | 0.2730 | |
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| 0.2352 | 1.01 | 1200 | 0.1479 | 0.2594 | |
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| 0.1988 | 1.26 | 1500 | 0.1428 | 0.2464 | |
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| 0.1879 | 1.51 | 1800 | 0.1388 | 0.2391 | |
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| 0.1757 | 1.76 | 2100 | 0.1244 | 0.2412 | |
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| 0.1683 | 2.01 | 2400 | 0.1278 | 0.2231 | |
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| 0.1475 | 2.27 | 2700 | 0.1231 | 0.2240 | |
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| 0.1438 | 2.52 | 3000 | 0.1219 | 0.2192 | |
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| 0.1422 | 2.77 | 3300 | 0.1216 | 0.2128 | |
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| 0.1337 | 3.02 | 3600 | 0.1145 | 0.2087 | |
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| 0.1212 | 3.27 | 3900 | 0.1131 | 0.2061 | |
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| 0.1195 | 3.52 | 4200 | 0.1159 | 0.2147 | |
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| 0.1169 | 3.78 | 4500 | 0.1111 | 0.2083 | |
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| 0.1162 | 4.03 | 4800 | 0.1116 | 0.2058 | |
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| 0.1073 | 4.28 | 5100 | 0.1138 | 0.2114 | |
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| 0.1011 | 4.53 | 5400 | 0.1103 | 0.2057 | |
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| 0.1008 | 4.78 | 5700 | 0.1096 | 0.2018 | |
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| 0.1016 | 5.03 | 6000 | 0.1045 | 0.2008 | |
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| 0.092 | 5.29 | 6300 | 0.1104 | 0.2008 | |
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| 0.0889 | 5.54 | 6600 | 0.1079 | 0.2005 | |
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| 0.0936 | 5.79 | 6900 | 0.1036 | 0.2026 | |
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| 0.0888 | 6.04 | 7200 | 0.1106 | 0.2109 | |
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| 0.0836 | 6.29 | 7500 | 0.1115 | 0.2103 | |
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| 0.0807 | 6.54 | 7800 | 0.1104 | 0.2045 | |
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| 0.0807 | 6.8 | 8100 | 0.1051 | 0.2039 | |
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| 0.0784 | 7.05 | 8400 | 0.1067 | 0.1947 | |
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| 0.0719 | 7.3 | 8700 | 0.1051 | 0.1957 | |
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| 0.0735 | 7.55 | 9000 | 0.1084 | 0.1894 | |
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| 0.0715 | 7.8 | 9300 | 0.1029 | 0.1916 | |
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| 0.0732 | 8.05 | 9600 | 0.1059 | 0.1894 | |
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| 0.0673 | 8.31 | 9900 | 0.1053 | 0.1890 | |
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| 0.0642 | 8.56 | 10200 | 0.1042 | 0.1879 | |
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| 0.0669 | 8.81 | 10500 | 0.1039 | 0.1877 | |
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| 0.0665 | 9.06 | 10800 | 0.1043 | 0.1881 | |
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| 0.0606 | 9.31 | 11100 | 0.1027 | 0.1870 | |
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| 0.0615 | 9.56 | 11400 | 0.1046 | 0.1887 | |
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| 0.0602 | 9.82 | 11700 | 0.1042 | 0.1892 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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