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malayalam_combined_

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  1. README.md +113 -0
  2. model.safetensors +1 -1
  3. runs/Aug20_21-09-57_akrishnan-25065.0-cl18lx.lsv.uni-saarland.de/events.out.tfevents.1724196174.akrishnan-25065.0-cl18lx.lsv.uni-saarland.de.109.0 +2 -2
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+ ---
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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: malayalam_combined_
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+ results: []
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+ ---
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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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+
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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/8awzxwte)
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+ # malayalam_combined_
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+
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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: 0.4324
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+ - Wer: 0.4182
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 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: 50
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+ - num_epochs: 10
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+ - mixed_precision_training: Native AMP
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Wer |
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+ |:-------------:|:------:|:-----:|:---------------:|:------:|
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+ | 0.8323 | 0.2031 | 500 | 0.8530 | 0.6902 |
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+ | 0.7407 | 0.4063 | 1000 | 0.7524 | 0.6254 |
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+ | 0.6897 | 0.6094 | 1500 | 0.6791 | 0.6136 |
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+ | 0.6347 | 0.8125 | 2000 | 0.6630 | 0.5799 |
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+ | 0.5888 | 1.0156 | 2500 | 0.6322 | 0.5833 |
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+ | 0.5818 | 1.2188 | 3000 | 0.6162 | 0.5866 |
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+ | 0.5732 | 1.4219 | 3500 | 0.6035 | 0.5519 |
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+ | 0.5451 | 1.6250 | 4000 | 0.5993 | 0.5303 |
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+ | 0.534 | 1.8282 | 4500 | 0.5844 | 0.5404 |
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+ | 0.5032 | 2.0313 | 5000 | 0.5632 | 0.5245 |
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+ | 0.4977 | 2.2344 | 5500 | 0.5445 | 0.5323 |
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+ | 0.5048 | 2.4375 | 6000 | 0.5302 | 0.5090 |
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+ | 0.5039 | 2.6407 | 6500 | 0.5409 | 0.5172 |
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+ | 0.4874 | 2.8438 | 7000 | 0.5326 | 0.5255 |
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+ | 0.4498 | 3.0469 | 7500 | 0.5139 | 0.4958 |
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+ | 0.4802 | 3.2501 | 8000 | 0.5199 | 0.5168 |
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+ | 0.452 | 3.4532 | 8500 | 0.5030 | 0.4990 |
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+ | 0.4526 | 3.6563 | 9000 | 0.4942 | 0.4841 |
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+ | 0.438 | 3.8594 | 9500 | 0.4896 | 0.4908 |
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+ | 0.4285 | 4.0626 | 10000 | 0.5156 | 0.4869 |
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+ | 0.4132 | 4.2657 | 10500 | 0.4971 | 0.4805 |
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+ | 0.4163 | 4.4688 | 11000 | 0.4845 | 0.4805 |
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+ | 0.3999 | 4.6719 | 11500 | 0.4804 | 0.4778 |
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+ | 0.3975 | 4.8751 | 12000 | 0.4743 | 0.4732 |
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+ | 0.3823 | 5.0782 | 12500 | 0.5002 | 0.4637 |
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+ | 0.3893 | 5.2813 | 13000 | 0.4793 | 0.4666 |
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+ | 0.3789 | 5.4845 | 13500 | 0.4742 | 0.4564 |
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+ | 0.3718 | 5.6876 | 14000 | 0.4731 | 0.4606 |
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+ | 0.3813 | 5.8907 | 14500 | 0.4609 | 0.4639 |
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+ | 0.3498 | 6.0938 | 15000 | 0.4645 | 0.4532 |
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+ | 0.348 | 6.2970 | 15500 | 0.4662 | 0.4508 |
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+ | 0.3263 | 6.5001 | 16000 | 0.4635 | 0.4493 |
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+ | 0.3404 | 6.7032 | 16500 | 0.4522 | 0.4399 |
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+ | 0.3352 | 6.9064 | 17000 | 0.4439 | 0.4459 |
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+ | 0.3192 | 7.1095 | 17500 | 0.4490 | 0.4486 |
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+ | 0.3035 | 7.3126 | 18000 | 0.4474 | 0.4381 |
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+ | 0.3175 | 7.5157 | 18500 | 0.4431 | 0.4374 |
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+ | 0.3161 | 7.7189 | 19000 | 0.4458 | 0.4344 |
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+ | 0.3244 | 7.9220 | 19500 | 0.4384 | 0.4377 |
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+ | 0.2795 | 8.1251 | 20000 | 0.4444 | 0.4326 |
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+ | 0.2975 | 8.3283 | 20500 | 0.4403 | 0.4307 |
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+ | 0.2854 | 8.5314 | 21000 | 0.4352 | 0.4278 |
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+ | 0.2856 | 8.7345 | 21500 | 0.4344 | 0.4288 |
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+ | 0.2764 | 8.9376 | 22000 | 0.4356 | 0.4239 |
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+ | 0.2546 | 9.1408 | 22500 | 0.4426 | 0.4229 |
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+ | 0.2703 | 9.3439 | 23000 | 0.4336 | 0.4237 |
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+ | 0.2438 | 9.5470 | 23500 | 0.4358 | 0.4218 |
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+ | 0.2612 | 9.7502 | 24000 | 0.4337 | 0.4195 |
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+ | 0.2698 | 9.9533 | 24500 | 0.4324 | 0.4182 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.43.0.dev0
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+ - Pytorch 1.14.0a0+44dac51
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+ - Datasets 2.16.1
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+ - Tokenizers 0.19.1
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