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--- |
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license: cc-by-4.0 |
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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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base_model: l3cube-pune/hing-roberta |
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model-index: |
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- name: hing-roberta-ours-run-5 |
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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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# hing-roberta-ours-run-5 |
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This model is a fine-tuned version of [l3cube-pune/hing-roberta](https://huggingface.co/l3cube-pune/hing-roberta) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.0980 |
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- Accuracy: 0.725 |
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- Precision: 0.6881 |
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- Recall: 0.6575 |
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- F1: 0.6651 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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| 0.9336 | 1.0 | 200 | 0.7394 | 0.675 | 0.6450 | 0.6509 | 0.6398 | |
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| 0.6924 | 2.0 | 400 | 0.9530 | 0.66 | 0.6285 | 0.5845 | 0.5551 | |
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| 0.4406 | 3.0 | 600 | 0.8914 | 0.68 | 0.6462 | 0.6527 | 0.6479 | |
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| 0.2493 | 4.0 | 800 | 1.7083 | 0.68 | 0.6441 | 0.6446 | 0.6426 | |
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| 0.1231 | 5.0 | 1000 | 1.9496 | 0.695 | 0.6570 | 0.6624 | 0.6591 | |
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| 0.0788 | 6.0 | 1200 | 2.5025 | 0.67 | 0.6209 | 0.6039 | 0.6011 | |
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| 0.0408 | 7.0 | 1400 | 2.2651 | 0.695 | 0.6594 | 0.6617 | 0.6517 | |
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| 0.0434 | 8.0 | 1600 | 2.4072 | 0.725 | 0.6941 | 0.6754 | 0.6710 | |
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| 0.0074 | 9.0 | 1800 | 2.7817 | 0.7 | 0.6535 | 0.6467 | 0.6488 | |
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| 0.023 | 10.0 | 2000 | 2.8578 | 0.7 | 0.6470 | 0.6353 | 0.6337 | |
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| 0.0151 | 11.0 | 2200 | 2.7783 | 0.695 | 0.6457 | 0.6373 | 0.6390 | |
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| 0.0108 | 12.0 | 2400 | 2.5953 | 0.695 | 0.6563 | 0.6586 | 0.6564 | |
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| 0.0192 | 13.0 | 2600 | 3.0715 | 0.705 | 0.6631 | 0.6326 | 0.6320 | |
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| 0.0149 | 14.0 | 2800 | 3.1048 | 0.715 | 0.6769 | 0.6450 | 0.6503 | |
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| 0.0205 | 15.0 | 3000 | 2.7812 | 0.71 | 0.6657 | 0.6538 | 0.6565 | |
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| 0.0024 | 16.0 | 3200 | 2.9304 | 0.72 | 0.6796 | 0.6537 | 0.6610 | |
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| 0.0033 | 17.0 | 3400 | 2.7170 | 0.73 | 0.6899 | 0.6760 | 0.6811 | |
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| 0.0056 | 18.0 | 3600 | 2.9693 | 0.72 | 0.6783 | 0.6560 | 0.6628 | |
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| 0.0015 | 19.0 | 3800 | 3.0943 | 0.72 | 0.6825 | 0.6541 | 0.6611 | |
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| 0.0017 | 20.0 | 4000 | 3.0980 | 0.725 | 0.6881 | 0.6575 | 0.6651 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu116 |
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- Tokenizers 0.13.2 |
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