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malayalam_combined_

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  1. README.md +45 -27
  2. model.safetensors +1 -1
  3. runs/Aug22_19-47-05_akrishnan-25174.0-cl17lx.lsv.uni-saarland.de/events.out.tfevents.1724362431.akrishnan-25174.0-cl17lx.lsv.uni-saarland.de.109.0 +2 -2
README.md CHANGED
@@ -13,13 +13,13 @@ model-index:
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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/e0c2wxc6)
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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: 0.5153
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- - Wer: 0.5077
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  ## Model description
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@@ -54,30 +54,48 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Wer |
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  |:-------------:|:------:|:-----:|:---------------:|:------:|
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- | 0.8243 | 0.2031 | 500 | 0.8413 | 0.6658 |
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- | 0.7336 | 0.4063 | 1000 | 0.7351 | 0.6251 |
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- | 0.6824 | 0.6094 | 1500 | 0.6786 | 0.5956 |
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- | 0.6489 | 0.8125 | 2000 | 0.6836 | 0.6075 |
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- | 0.585 | 1.0156 | 2500 | 0.6295 | 0.5864 |
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- | 0.5917 | 1.2188 | 3000 | 0.6166 | 0.5579 |
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- | 0.56 | 1.4219 | 3500 | 0.6006 | 0.5646 |
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- | 0.5736 | 1.6250 | 4000 | 0.6268 | 0.5643 |
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- | 0.5821 | 1.8282 | 4500 | 0.6216 | 0.5786 |
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- | 0.5505 | 2.0313 | 5000 | 0.5705 | 0.5379 |
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- | 0.5065 | 2.2344 | 5500 | 0.5864 | 0.5460 |
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- | 0.5004 | 2.4375 | 6000 | 0.5555 | 0.5259 |
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- | 0.5327 | 2.6407 | 6500 | 0.5539 | 0.5255 |
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- | 0.5148 | 2.8438 | 7000 | 0.5584 | 0.5457 |
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- | 0.4751 | 3.0469 | 7500 | 0.5389 | 0.5208 |
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- | 0.4779 | 3.2501 | 8000 | 0.5284 | 0.5102 |
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- | 0.4874 | 3.4532 | 8500 | 0.5300 | 0.5084 |
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- | 0.4955 | 3.6563 | 9000 | 0.5248 | 0.5125 |
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- | 0.4961 | 3.8594 | 9500 | 0.5116 | 0.5061 |
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- | 0.4449 | 4.0626 | 10000 | 0.5257 | 0.5122 |
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- | 0.48 | 4.2657 | 10500 | 0.5254 | 0.5046 |
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- | 0.4513 | 4.4688 | 11000 | 0.5364 | 0.5232 |
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- | 0.4698 | 4.6719 | 11500 | 0.5293 | 0.5106 |
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- | 0.4674 | 4.8751 | 12000 | 0.5153 | 0.5077 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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/m81qlwga)
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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: 0.4712
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+ - Wer: 0.4649
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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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+ | 0.8201 | 0.2031 | 500 | 0.8317 | 0.6757 |
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+ | 0.7455 | 0.4063 | 1000 | 0.7271 | 0.6119 |
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+ | 0.6928 | 0.6094 | 1500 | 0.6823 | 0.6083 |
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+ | 0.6497 | 0.8125 | 2000 | 0.6775 | 0.5955 |
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+ | 0.5968 | 1.0156 | 2500 | 0.6554 | 0.5837 |
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+ | 0.594 | 1.2188 | 3000 | 0.6127 | 0.5772 |
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+ | 0.558 | 1.4219 | 3500 | 0.6149 | 0.5574 |
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+ | 0.5906 | 1.6250 | 4000 | 0.5856 | 0.5485 |
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+ | 0.5846 | 1.8282 | 4500 | 0.5894 | 0.5413 |
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+ | 0.5504 | 2.0313 | 5000 | 0.5571 | 0.5310 |
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+ | 0.5059 | 2.2344 | 5500 | 0.5735 | 0.5542 |
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+ | 0.5019 | 2.4375 | 6000 | 0.5555 | 0.5278 |
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+ | 0.5274 | 2.6407 | 6500 | 0.5592 | 0.5111 |
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+ | 0.5113 | 2.8438 | 7000 | 0.5391 | 0.5318 |
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+ | 0.4624 | 3.0469 | 7500 | 0.5191 | 0.5189 |
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+ | 0.4696 | 3.2501 | 8000 | 0.5312 | 0.5072 |
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+ | 0.4866 | 3.4532 | 8500 | 0.5208 | 0.5167 |
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+ | 0.4817 | 3.6563 | 9000 | 0.5141 | 0.4966 |
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+ | 0.5053 | 3.8594 | 9500 | 0.5104 | 0.5010 |
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+ | 0.4541 | 4.0626 | 10000 | 0.5247 | 0.5130 |
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+ | 0.4761 | 4.2657 | 10500 | 0.5287 | 0.5157 |
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+ | 0.4385 | 4.4688 | 11000 | 0.5196 | 0.5018 |
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+ | 0.4698 | 4.6719 | 11500 | 0.5286 | 0.5120 |
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+ | 0.4631 | 4.8751 | 12000 | 0.5045 | 0.4957 |
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+ | 0.4269 | 5.0782 | 12500 | 0.5102 | 0.5018 |
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+ | 0.4253 | 5.2813 | 13000 | 0.5085 | 0.4949 |
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+ | 0.425 | 5.4845 | 13500 | 0.5209 | 0.4894 |
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+ | 0.4391 | 5.6876 | 14000 | 0.5037 | 0.4900 |
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+ | 0.4206 | 5.8907 | 14500 | 0.5265 | 0.4802 |
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+ | 0.4087 | 6.0938 | 15000 | 0.5044 | 0.4829 |
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+ | 0.4112 | 6.2970 | 15500 | 0.4962 | 0.4860 |
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+ | 0.3864 | 6.5001 | 16000 | 0.4823 | 0.4777 |
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+ | 0.4403 | 6.7032 | 16500 | 0.4898 | 0.4808 |
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+ | 0.3942 | 6.9064 | 17000 | 0.4821 | 0.4808 |
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+ | 0.386 | 7.1095 | 17500 | 0.4804 | 0.4789 |
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+ | 0.3752 | 7.3126 | 18000 | 0.4735 | 0.4662 |
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+ | 0.3863 | 7.5157 | 18500 | 0.4680 | 0.4662 |
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+ | 0.3767 | 7.7189 | 19000 | 0.4692 | 0.4610 |
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+ | 0.3942 | 7.9220 | 19500 | 0.4700 | 0.4721 |
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+ | 0.3502 | 8.1251 | 20000 | 0.4759 | 0.4742 |
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+ | 0.3504 | 8.3283 | 20500 | 0.4702 | 0.4653 |
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+ | 0.3594 | 8.5314 | 21000 | 0.4712 | 0.4649 |
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  ### Framework versions
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