End of training
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README.md
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/krishnan-aravind/huggingface/runs/
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# malayalam_combined_
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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.
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- Wer: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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### Framework versions
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/krishnan-aravind/huggingface/runs/2ay2lri6)
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# malayalam_combined_
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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: 0.4789
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- Wer: 0.4611
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:------:|:-----:|:---------------:|:------:|
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| 0.8401 | 0.2031 | 500 | 0.8498 | 0.7068 |
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| 0.7367 | 0.4063 | 1000 | 0.7280 | 0.6183 |
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| 0.6974 | 0.6094 | 1500 | 0.7055 | 0.6113 |
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| 0.6493 | 0.8125 | 2000 | 0.6765 | 0.5989 |
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| 0.5905 | 1.0156 | 2500 | 0.6521 | 0.5937 |
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| 0.606 | 1.2188 | 3000 | 0.6192 | 0.5639 |
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| 0.5601 | 1.4219 | 3500 | 0.6242 | 0.5526 |
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| 0.5868 | 1.6250 | 4000 | 0.6118 | 0.5559 |
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| 0.5792 | 1.8282 | 4500 | 0.5879 | 0.5523 |
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| 0.554 | 2.0313 | 5000 | 0.5775 | 0.5501 |
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| 0.505 | 2.2344 | 5500 | 0.5640 | 0.5466 |
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| 0.5055 | 2.4375 | 6000 | 0.5668 | 0.5298 |
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| 0.5228 | 2.6407 | 6500 | 0.5410 | 0.5178 |
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| 0.5186 | 2.8438 | 7000 | 0.5785 | 0.5540 |
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| 0.4811 | 3.0469 | 7500 | 0.5446 | 0.5408 |
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| 0.4794 | 3.2501 | 8000 | 0.5333 | 0.5102 |
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| 0.4952 | 3.4532 | 8500 | 0.5205 | 0.5135 |
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| 0.4761 | 3.6563 | 9000 | 0.5218 | 0.5092 |
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| 0.5079 | 3.8594 | 9500 | 0.5192 | 0.5166 |
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| 0.4407 | 4.0626 | 10000 | 0.5207 | 0.5054 |
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| 0.4711 | 4.2657 | 10500 | 0.5215 | 0.5086 |
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| 0.4396 | 4.4688 | 11000 | 0.5289 | 0.5145 |
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| 0.4667 | 4.6719 | 11500 | 0.5144 | 0.5015 |
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| 0.4518 | 4.8751 | 12000 | 0.5222 | 0.5112 |
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| 0.4211 | 5.0782 | 12500 | 0.5094 | 0.4897 |
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| 0.43 | 5.2813 | 13000 | 0.5242 | 0.5011 |
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| 0.4218 | 5.4845 | 13500 | 0.5132 | 0.4905 |
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| 0.4279 | 5.6876 | 14000 | 0.5153 | 0.4883 |
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| 0.4341 | 5.8907 | 14500 | 0.5321 | 0.4899 |
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| 0.409 | 6.0938 | 15000 | 0.5079 | 0.4884 |
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| 0.4111 | 6.2970 | 15500 | 0.5067 | 0.4844 |
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| 0.3781 | 6.5001 | 16000 | 0.5091 | 0.4643 |
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| 0.4274 | 6.7032 | 16500 | 0.4842 | 0.4831 |
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| 0.4009 | 6.9064 | 17000 | 0.4791 | 0.4738 |
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| 0.3895 | 7.1095 | 17500 | 0.4786 | 0.4691 |
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| 0.3788 | 7.3126 | 18000 | 0.4845 | 0.4691 |
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| 0.3909 | 7.5157 | 18500 | 0.4869 | 0.4612 |
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| 0.3795 | 7.7189 | 19000 | 0.4729 | 0.4606 |
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| 0.3874 | 7.9220 | 19500 | 0.4667 | 0.4655 |
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| 0.3472 | 8.1251 | 20000 | 0.4718 | 0.4720 |
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| 0.3634 | 8.3283 | 20500 | 0.4767 | 0.4616 |
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| 0.3545 | 8.5314 | 21000 | 0.4821 | 0.4640 |
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| 0.37 | 8.7345 | 21500 | 0.4789 | 0.4611 |
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### Framework versions
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model.safetensors
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