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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-mbert |
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model-index: |
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- name: hing-mbert-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-mbert-ours-run-5 |
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This model is a fine-tuned version of [l3cube-pune/hing-mbert](https://huggingface.co/l3cube-pune/hing-mbert) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 3.2437 |
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- Accuracy: 0.665 |
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- Precision: 0.6223 |
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- Recall: 0.5991 |
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- F1: 0.6039 |
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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: 16 |
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- eval_batch_size: 16 |
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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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| 0.9643 | 1.0 | 100 | 0.7996 | 0.69 | 0.6596 | 0.6593 | 0.6521 | |
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| 0.6951 | 2.0 | 200 | 1.0464 | 0.66 | 0.6597 | 0.5831 | 0.5734 | |
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| 0.4245 | 3.0 | 300 | 0.9640 | 0.64 | 0.6025 | 0.6033 | 0.6010 | |
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| 0.238 | 4.0 | 400 | 1.6744 | 0.68 | 0.7095 | 0.6445 | 0.6359 | |
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| 0.1477 | 5.0 | 500 | 1.7115 | 0.665 | 0.6362 | 0.6422 | 0.6360 | |
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| 0.1206 | 6.0 | 600 | 2.0459 | 0.635 | 0.5749 | 0.5752 | 0.5726 | |
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| 0.0528 | 7.0 | 700 | 2.5698 | 0.66 | 0.6230 | 0.5904 | 0.5985 | |
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| 0.0525 | 8.0 | 800 | 2.2729 | 0.625 | 0.5741 | 0.5860 | 0.5733 | |
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| 0.0174 | 9.0 | 900 | 2.6227 | 0.635 | 0.6099 | 0.6044 | 0.6019 | |
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| 0.0088 | 10.0 | 1000 | 2.8854 | 0.63 | 0.5699 | 0.5676 | 0.5680 | |
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| 0.0085 | 11.0 | 1100 | 3.2173 | 0.655 | 0.6043 | 0.5771 | 0.5821 | |
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| 0.0121 | 12.0 | 1200 | 3.1270 | 0.665 | 0.6214 | 0.5903 | 0.5971 | |
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| 0.0141 | 13.0 | 1300 | 2.6648 | 0.655 | 0.5981 | 0.5978 | 0.5961 | |
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| 0.0116 | 14.0 | 1400 | 3.1711 | 0.665 | 0.6192 | 0.5915 | 0.5971 | |
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| 0.007 | 15.0 | 1500 | 3.0954 | 0.66 | 0.6156 | 0.5961 | 0.6009 | |
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| 0.0037 | 16.0 | 1600 | 3.3065 | 0.65 | 0.6027 | 0.5791 | 0.5824 | |
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| 0.0031 | 17.0 | 1700 | 3.1715 | 0.665 | 0.6177 | 0.5999 | 0.6048 | |
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| 0.0021 | 18.0 | 1800 | 3.1602 | 0.665 | 0.6220 | 0.6029 | 0.6082 | |
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| 0.0021 | 19.0 | 1900 | 3.2027 | 0.655 | 0.6096 | 0.5893 | 0.5937 | |
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| 0.0018 | 20.0 | 2000 | 3.2437 | 0.665 | 0.6223 | 0.5991 | 0.6039 | |
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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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