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 results
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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/7l3na48x)
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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: 0.5025
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- Wer: 0.4256
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## Model description
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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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| 0.8238 | 0.2031 | 500 | 0.8281 | 0.6745 |
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| 0.7415 | 0.4063 | 1000 | 0.7477 | 0.6446 |
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| 0.6913 | 0.6094 | 1500 | 0.6962 | 0.6072 |
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| 0.6401 | 0.8125 | 2000 | 0.6981 | 0.5929 |
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| 0.5864 | 1.0156 | 2500 | 0.6809 | 0.5712 |
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| 0.5843 | 1.2188 | 3000 | 0.6125 | 0.5691 |
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| 0.5547 | 1.4219 | 3500 | 0.6110 | 0.5616 |
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| 0.5657 | 1.6250 | 4000 | 0.5882 | 0.5464 |
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| 0.5809 | 1.8282 | 4500 | 0.5776 | 0.5481 |
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| 0.5464 | 2.0313 | 5000 | 0.5689 | 0.5278 |
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| 0.4974 | 2.2344 | 5500 | 0.5926 | 0.5428 |
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| 0.5012 | 2.4375 | 6000 | 0.5622 | 0.5384 |
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| 0.5162 | 2.6407 | 6500 | 0.5697 | 0.5179 |
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| 0.5006 | 2.8438 | 7000 | 0.5357 | 0.5375 |
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| 0.4661 | 3.0469 | 7500 | 0.5255 | 0.5255 |
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| 0.4658 | 3.2501 | 8000 | 0.5182 | 0.5002 |
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| 0.4716 | 3.4532 | 8500 | 0.5176 | 0.5044 |
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| 0.4658 | 3.6563 | 9000 | 0.5139 | 0.5061 |
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| 0.5031 | 3.8594 | 9500 | 0.5114 | 0.5068 |
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| 0.4482 | 4.0626 | 10000 | 0.5331 | 0.5101 |
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| 0.4678 | 4.2657 | 10500 | 0.5165 | 0.5126 |
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| 0.4353 | 4.4688 | 11000 | 0.5292 | 0.5112 |
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| 0.4711 | 4.6719 | 11500 | 0.5178 | 0.4979 |
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| 0.4574 | 4.8751 | 12000 | 0.5215 | 0.5100 |
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| 0.4246 | 5.0782 | 12500 | 0.5190 | 0.4938 |
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| 0.4164 | 5.2813 | 13000 | 0.5504 | 0.4898 |
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| 0.4181 | 5.4845 | 13500 | 0.5045 | 0.4979 |
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| 0.4279 | 5.6876 | 14000 | 0.5118 | 0.4932 |
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| 0.4244 | 5.8907 | 14500 | 0.4970 | 0.4842 |
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| 0.4038 | 6.0938 | 15000 | 0.5013 | 0.4776 |
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| 0.4179 | 6.2970 | 15500 | 0.5061 | 0.4762 |
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| 0.3812 | 6.5001 | 16000 | 0.4987 | 0.4689 |
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| 0.4217 | 6.7032 | 16500 | 0.4986 | 0.4807 |
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| 0.3989 | 6.9064 | 17000 | 0.4905 | 0.4709 |
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| 0.3741 | 7.1095 | 17500 | 0.4842 | 0.4700 |
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| 0.3743 | 7.3126 | 18000 | 0.4869 | 0.4734 |
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| 0.3785 | 7.5157 | 18500 | 0.4692 | 0.4690 |
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| 0.3759 | 7.7189 | 19000 | 0.4691 | 0.4646 |
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| 0.3809 | 7.9220 | 19500 | 0.4736 | 0.4720 |
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| 0.3499 | 8.1251 | 20000 | 0.4787 | 0.4691 |
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| 0.3523 | 8.3283 | 20500 | 0.4689 | 0.4680 |
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| 0.3551 | 8.5314 | 21000 | 0.4792 | 0.4567 |
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| 0.3672 | 8.7345 | 21500 | 0.4760 | 0.4652 |
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| 0.3554 | 8.9376 | 22000 | 0.4649 | 0.4648 |
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| 0.3182 | 9.1408 | 22500 | 0.4853 | 0.4565 |
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| 0.3412 | 9.3439 | 23000 | 0.4958 | 0.4616 |
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| 0.3494 | 9.5470 | 23500 | 0.4971 | 0.4527 |
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| 0.3426 | 9.7502 | 24000 | 0.4959 | 0.4554 |
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| 0.3365 | 9.9533 | 24500 | 0.4659 | 0.4582 |
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| 0.3179 | 10.1564 | 25000 | 0.4807 | 0.4445 |
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| 0.3361 | 10.3595 | 25500 | 0.4700 | 0.4535 |
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| 0.3234 | 10.5627 | 26000 | 0.4562 | 0.4542 |
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| 0.3296 | 10.7658 | 26500 | 0.4682 | 0.4452 |
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| 0.3148 | 10.9689 | 27000 | 0.4716 | 0.4521 |
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| 0.3112 | 11.1720 | 27500 | 0.4537 | 0.4473 |
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| 0.3246 | 11.3752 | 28000 | 0.4594 | 0.4444 |
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| 0.3062 | 11.5783 | 28500 | 0.4544 | 0.4445 |
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| 0.2979 | 11.7814 | 29000 | 0.4531 | 0.4516 |
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| 0.3108 | 11.9846 | 29500 | 0.4514 | 0.4428 |
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| 0.2876 | 12.1877 | 30000 | 0.4598 | 0.4402 |
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| 0.2911 | 12.3908 | 30500 | 0.4554 | 0.4426 |
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| 0.2963 | 12.5939 | 31000 | 0.4641 | 0.4483 |
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| 0.296 | 12.7971 | 31500 | 0.4575 | 0.4394 |
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| 0.2777 | 13.0002 | 32000 | 0.4586 | 0.4444 |
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| 0.2782 | 13.2033 | 32500 | 0.4498 | 0.4461 |
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| 0.2695 | 13.4065 | 33000 | 0.4696 | 0.4450 |
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| 0.286 | 13.6096 | 33500 | 0.4630 | 0.4383 |
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| 0.279 | 13.8127 | 34000 | 0.4618 | 0.4401 |
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| 0.2584 | 14.0158 | 34500 | 0.4526 | 0.4356 |
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| 0.267 | 14.2190 | 35000 | 0.4726 | 0.4297 |
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| 0.2667 | 14.4221 | 35500 | 0.4572 | 0.4308 |
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| 0.2592 | 14.6252 | 36000 | 0.4795 | 0.4325 |
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| 0.2592 | 14.8284 | 36500 | 0.4528 | 0.4303 |
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| 0.2644 | 15.0315 | 37000 | 0.4604 | 0.4306 |
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| 0.2312 | 15.2346 | 37500 | 0.4632 | 0.4367 |
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| 0.2408 | 15.4377 | 38000 | 0.4670 | 0.4324 |
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| 0.2489 | 15.6409 | 38500 | 0.4580 | 0.4253 |
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| 0.2652 | 15.8440 | 39000 | 0.4581 | 0.4375 |
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| 0.2367 | 16.0471 | 39500 | 0.4770 | 0.4213 |
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| 0.2366 | 16.2503 | 40000 | 0.4751 | 0.4243 |
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| 0.2267 | 16.4534 | 40500 | 0.4622 | 0.4282 |
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| 0.2461 | 16.6565 | 41000 | 0.4671 | 0.4249 |
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| 0.2326 | 16.8596 | 41500 | 0.4736 | 0.4293 |
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| 0.2121 | 17.0628 | 42000 | 0.4905 | 0.4300 |
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| 0.222 | 17.2659 | 42500 | 0.4782 | 0.4261 |
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| 0.2202 | 17.4690 | 43000 | 0.4670 | 0.4250 |
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| 0.2141 | 17.6722 | 43500 | 0.4688 | 0.4259 |
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| 0.2231 | 17.8753 | 44000 | 0.4718 | 0.4254 |
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| 0.2144 | 18.0784 | 44500 | 0.5025 | 0.4256 |
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### Framework versions
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 2423199960
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version https://git-lfs.github.com/spec/v1
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size 2423199960
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