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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-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-malayalam-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 the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1097 |
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- Wer: 0.0913 |
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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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- training_steps: 38000 |
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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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| 0.3486 | 0.2859 | 2000 | 0.3181 | 0.4042 | |
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| 0.291 | 0.5718 | 4000 | 0.2474 | 0.3020 | |
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| 0.2196 | 0.8577 | 6000 | 0.2151 | 0.2710 | |
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| 0.1915 | 1.1437 | 8000 | 0.2131 | 0.2488 | |
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| 0.1811 | 1.4295 | 10000 | 0.1786 | 0.2204 | |
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| 0.1881 | 1.7154 | 12000 | 0.1720 | 0.2061 | |
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| 0.1598 | 2.0014 | 14000 | 0.1768 | 0.1834 | |
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| 0.1429 | 2.2873 | 16000 | 0.1741 | 0.1708 | |
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| 0.1389 | 2.5732 | 18000 | 0.1646 | 0.1560 | |
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| 0.1314 | 2.8591 | 20000 | 0.1387 | 0.1490 | |
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| 0.0953 | 3.1451 | 22000 | 0.1457 | 0.1373 | |
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| 0.0915 | 3.4310 | 24000 | 0.1287 | 0.1238 | |
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| 0.0871 | 3.7169 | 26000 | 0.1255 | 0.1145 | |
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| 0.0903 | 4.0029 | 28000 | 0.1181 | 0.1069 | |
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| 0.0723 | 4.2887 | 30000 | 0.1226 | 0.1022 | |
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| 0.0599 | 4.5746 | 32000 | 0.1115 | 0.0992 | |
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| 0.0576 | 4.8605 | 34000 | 0.1087 | 0.0977 | |
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| 0.0473 | 5.1465 | 36000 | 0.1079 | 0.0928 | |
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| 0.0485 | 5.4324 | 38000 | 0.1097 | 0.0913 | |
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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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