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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-CM-run-3 |
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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-CM-run-3 |
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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: 2.6968 |
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- Accuracy: 0.7565 |
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- Precision: 0.7045 |
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- Recall: 0.7064 |
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- F1: 0.7050 |
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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: 3e-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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| 0.8232 | 1.0 | 497 | 0.7145 | 0.6620 | 0.6319 | 0.6585 | 0.6167 | |
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| 0.5799 | 2.0 | 994 | 0.7155 | 0.7203 | 0.6718 | 0.6928 | 0.6672 | |
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| 0.4152 | 3.0 | 1491 | 0.8823 | 0.7485 | 0.6962 | 0.7136 | 0.7022 | |
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| 0.2657 | 4.0 | 1988 | 1.4502 | 0.7465 | 0.6945 | 0.7037 | 0.6968 | |
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| 0.16 | 5.0 | 2485 | 2.0667 | 0.7465 | 0.6890 | 0.6827 | 0.6855 | |
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| 0.0945 | 6.0 | 2982 | 2.0120 | 0.7565 | 0.7091 | 0.7159 | 0.7103 | |
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| 0.0802 | 7.0 | 3479 | 2.2426 | 0.7686 | 0.7253 | 0.7065 | 0.7088 | |
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| 0.059 | 8.0 | 3976 | 2.3472 | 0.7425 | 0.6844 | 0.6881 | 0.6861 | |
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| 0.041 | 9.0 | 4473 | 2.4801 | 0.7666 | 0.7258 | 0.7144 | 0.7145 | |
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| 0.0307 | 10.0 | 4970 | 2.6317 | 0.7545 | 0.7102 | 0.7021 | 0.7019 | |
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| 0.0471 | 11.0 | 5467 | 2.4626 | 0.7364 | 0.6836 | 0.6780 | 0.6788 | |
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| 0.0282 | 12.0 | 5964 | 2.3949 | 0.7586 | 0.7067 | 0.7108 | 0.7087 | |
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| 0.0267 | 13.0 | 6461 | 2.4750 | 0.7465 | 0.6938 | 0.6921 | 0.6921 | |
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| 0.0274 | 14.0 | 6958 | 2.5942 | 0.7565 | 0.7022 | 0.7062 | 0.7039 | |
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| 0.0212 | 15.0 | 7455 | 2.6728 | 0.7404 | 0.6851 | 0.6893 | 0.6867 | |
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| 0.026 | 16.0 | 7952 | 2.6683 | 0.7565 | 0.7064 | 0.7122 | 0.7085 | |
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| 0.0175 | 17.0 | 8449 | 2.6646 | 0.7505 | 0.7030 | 0.7087 | 0.7039 | |
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| 0.0126 | 18.0 | 8946 | 2.6948 | 0.7565 | 0.7021 | 0.7039 | 0.7030 | |
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| 0.0065 | 19.0 | 9443 | 2.6984 | 0.7565 | 0.7045 | 0.7064 | 0.7050 | |
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| 0.0103 | 20.0 | 9940 | 2.6968 | 0.7565 | 0.7045 | 0.7064 | 0.7050 | |
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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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