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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-4 |
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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-4 |
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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: 3.0173 |
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- Accuracy: 0.68 |
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- Precision: 0.6330 |
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- Recall: 0.6325 |
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- F1: 0.6320 |
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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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| 0.9781 | 1.0 | 100 | 0.8852 | 0.55 | 0.4044 | 0.5284 | 0.4211 | |
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| 0.7568 | 2.0 | 200 | 0.8110 | 0.655 | 0.5994 | 0.6013 | 0.5762 | |
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| 0.5121 | 3.0 | 300 | 0.9735 | 0.65 | 0.6145 | 0.6131 | 0.5965 | |
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| 0.314 | 4.0 | 400 | 1.1324 | 0.65 | 0.6305 | 0.6355 | 0.6266 | |
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| 0.1298 | 5.0 | 500 | 2.8247 | 0.61 | 0.5804 | 0.5087 | 0.5092 | |
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| 0.1013 | 6.0 | 600 | 2.8183 | 0.635 | 0.6212 | 0.5674 | 0.5667 | |
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| 0.0989 | 7.0 | 700 | 2.3235 | 0.635 | 0.5944 | 0.5922 | 0.5916 | |
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| 0.0481 | 8.0 | 800 | 2.5240 | 0.68 | 0.6334 | 0.6172 | 0.6221 | |
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| 0.018 | 9.0 | 900 | 2.6782 | 0.65 | 0.6123 | 0.6054 | 0.6062 | |
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| 0.0285 | 10.0 | 1000 | 2.3400 | 0.67 | 0.6206 | 0.6327 | 0.6189 | |
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| 0.014 | 11.0 | 1100 | 2.6558 | 0.66 | 0.6098 | 0.5992 | 0.6018 | |
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| 0.0085 | 12.0 | 1200 | 2.9366 | 0.66 | 0.6076 | 0.5961 | 0.5991 | |
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| 0.0106 | 13.0 | 1300 | 2.8567 | 0.665 | 0.6198 | 0.6193 | 0.6186 | |
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| 0.0097 | 14.0 | 1400 | 3.1526 | 0.64 | 0.6089 | 0.5975 | 0.5954 | |
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| 0.0022 | 15.0 | 1500 | 2.7305 | 0.69 | 0.6404 | 0.6404 | 0.6398 | |
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| 0.0016 | 16.0 | 1600 | 2.7670 | 0.69 | 0.6418 | 0.6434 | 0.6425 | |
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| 0.0017 | 17.0 | 1700 | 2.8193 | 0.7 | 0.6533 | 0.6566 | 0.6546 | |
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| 0.0009 | 18.0 | 1800 | 2.9959 | 0.685 | 0.6400 | 0.6389 | 0.6384 | |
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| 0.0006 | 19.0 | 1900 | 3.0153 | 0.68 | 0.6330 | 0.6325 | 0.6320 | |
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| 0.0005 | 20.0 | 2000 | 3.0173 | 0.68 | 0.6330 | 0.6325 | 0.6320 | |
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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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