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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: indic-bert-finetuned-code-mixed-DS |
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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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# indic-bert-finetuned-code-mixed-DS |
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This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co./ai4bharat/indic-bert) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8647 |
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- Accuracy: 0.5795 |
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- Precision: 0.5485 |
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- Recall: 0.5287 |
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- F1: 0.4391 |
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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: 1e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 43 |
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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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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 1.0937 | 2.0 | 497 | 1.0813 | 0.3602 | 0.3587 | 0.4257 | 0.2834 | |
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| 1.0189 | 3.99 | 994 | 0.9482 | 0.5493 | 0.3887 | 0.5246 | 0.4080 | |
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| 0.9208 | 5.99 | 1491 | 0.9002 | 0.5714 | 0.3813 | 0.5292 | 0.4170 | |
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| 0.8803 | 7.98 | 1988 | 0.8758 | 0.5654 | 0.3889 | 0.5300 | 0.4159 | |
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| 0.8482 | 9.98 | 2485 | 0.8657 | 0.5795 | 0.3867 | 0.5365 | 0.4228 | |
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| 0.8293 | 11.98 | 2982 | 0.8734 | 0.5835 | 0.3796 | 0.5298 | 0.4214 | |
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| 0.8131 | 13.97 | 3479 | 0.8567 | 0.5835 | 0.5018 | 0.5414 | 0.4350 | |
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| 0.8 | 15.97 | 3976 | 0.8547 | 0.5835 | 0.5610 | 0.5460 | 0.4361 | |
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| 0.7933 | 17.96 | 4473 | 0.8650 | 0.5775 | 0.5317 | 0.5252 | 0.4373 | |
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| 0.7835 | 19.96 | 4970 | 0.8647 | 0.5795 | 0.5485 | 0.5287 | 0.4391 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.10.1+cu111 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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