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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-ours-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-ours-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: 2.1832 |
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- Accuracy: 0.655 |
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- Precision: 0.6023 |
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- Recall: 0.6027 |
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- F1: 0.6025 |
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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-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 32 |
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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: 25 |
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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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| 1.0681 | 0.99 | 99 | 1.0180 | 0.365 | 0.3435 | 0.4038 | 0.2773 | |
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| 0.9384 | 1.98 | 198 | 0.8475 | 0.62 | 0.6235 | 0.5610 | 0.4821 | |
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| 0.8201 | 2.97 | 297 | 0.8187 | 0.68 | 0.6839 | 0.6086 | 0.5812 | |
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| 0.7178 | 3.96 | 396 | 0.7717 | 0.7 | 0.7117 | 0.6670 | 0.6470 | |
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| 0.62 | 4.95 | 495 | 0.7839 | 0.66 | 0.6165 | 0.6244 | 0.6174 | |
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| 0.5135 | 5.94 | 594 | 0.8392 | 0.675 | 0.6270 | 0.6234 | 0.6246 | |
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| 0.4073 | 6.93 | 693 | 0.8930 | 0.665 | 0.6251 | 0.6254 | 0.6240 | |
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| 0.3365 | 7.92 | 792 | 0.9362 | 0.675 | 0.6298 | 0.6276 | 0.6242 | |
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| 0.2719 | 8.91 | 891 | 1.0108 | 0.685 | 0.6388 | 0.6293 | 0.6326 | |
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| 0.2007 | 9.9 | 990 | 1.1214 | 0.675 | 0.6300 | 0.6299 | 0.6290 | |
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| 0.1567 | 10.89 | 1089 | 1.1367 | 0.67 | 0.6193 | 0.6212 | 0.6178 | |
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| 0.1074 | 11.88 | 1188 | 1.3157 | 0.65 | 0.6292 | 0.6317 | 0.6227 | |
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| 0.0821 | 12.87 | 1287 | 1.5412 | 0.665 | 0.6415 | 0.6330 | 0.6259 | |
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| 0.0588 | 13.86 | 1386 | 1.7215 | 0.64 | 0.5862 | 0.5869 | 0.5865 | |
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| 0.0337 | 14.85 | 1485 | 1.7556 | 0.64 | 0.6078 | 0.6082 | 0.6032 | |
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| 0.0244 | 15.84 | 1584 | 1.8713 | 0.66 | 0.6173 | 0.6186 | 0.6158 | |
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| 0.0166 | 16.83 | 1683 | 1.9666 | 0.66 | 0.5995 | 0.5973 | 0.5973 | |
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| 0.0124 | 17.82 | 1782 | 1.9245 | 0.66 | 0.6165 | 0.6194 | 0.6163 | |
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| 0.0079 | 18.81 | 1881 | 2.0814 | 0.65 | 0.6026 | 0.6023 | 0.6012 | |
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| 0.0051 | 19.8 | 1980 | 2.1029 | 0.645 | 0.6014 | 0.5986 | 0.5975 | |
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| 0.0031 | 20.79 | 2079 | 2.1155 | 0.655 | 0.6029 | 0.6027 | 0.6023 | |
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| 0.0029 | 21.78 | 2178 | 2.1221 | 0.655 | 0.6 | 0.6000 | 0.5999 | |
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| 0.0021 | 22.77 | 2277 | 2.2065 | 0.65 | 0.5917 | 0.5898 | 0.5905 | |
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| 0.0017 | 23.76 | 2376 | 2.1903 | 0.65 | 0.5910 | 0.5898 | 0.5902 | |
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| 0.0016 | 24.75 | 2475 | 2.1832 | 0.655 | 0.6023 | 0.6027 | 0.6025 | |
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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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