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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-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-mbert-ours-run-3 |
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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: 2.9769 |
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- Accuracy: 0.675 |
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- Precision: 0.6433 |
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- Recall: 0.6344 |
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- F1: 0.6344 |
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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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| 0.9089 | 1.0 | 100 | 1.0993 | 0.635 | 0.6487 | 0.5304 | 0.5060 | |
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| 0.6657 | 2.0 | 200 | 0.8138 | 0.645 | 0.6550 | 0.6482 | 0.6234 | |
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| 0.3858 | 3.0 | 300 | 1.1334 | 0.665 | 0.6162 | 0.6061 | 0.5995 | |
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| 0.208 | 4.0 | 400 | 1.9041 | 0.685 | 0.6488 | 0.6169 | 0.6087 | |
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| 0.0996 | 5.0 | 500 | 2.3735 | 0.645 | 0.5867 | 0.5781 | 0.5794 | |
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| 0.0296 | 6.0 | 600 | 2.5772 | 0.665 | 0.6284 | 0.6208 | 0.6198 | |
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| 0.0623 | 7.0 | 700 | 2.8906 | 0.655 | 0.6040 | 0.5916 | 0.5926 | |
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| 0.0395 | 8.0 | 800 | 2.6567 | 0.675 | 0.6279 | 0.6254 | 0.6219 | |
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| 0.029 | 9.0 | 900 | 2.9277 | 0.655 | 0.6208 | 0.5950 | 0.5991 | |
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| 0.0194 | 10.0 | 1000 | 2.7362 | 0.665 | 0.6241 | 0.6208 | 0.6190 | |
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| 0.0092 | 11.0 | 1100 | 2.5561 | 0.68 | 0.6396 | 0.6401 | 0.6385 | |
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| 0.0059 | 12.0 | 1200 | 3.1112 | 0.675 | 0.6350 | 0.5967 | 0.6042 | |
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| 0.0133 | 13.0 | 1300 | 2.5269 | 0.685 | 0.6520 | 0.6607 | 0.6519 | |
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| 0.0051 | 14.0 | 1400 | 2.8736 | 0.68 | 0.6381 | 0.6158 | 0.6134 | |
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| 0.0044 | 15.0 | 1500 | 2.9132 | 0.675 | 0.6336 | 0.6180 | 0.6200 | |
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| 0.0029 | 16.0 | 1600 | 2.8701 | 0.675 | 0.6337 | 0.6214 | 0.6233 | |
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| 0.0015 | 17.0 | 1700 | 2.8115 | 0.69 | 0.6475 | 0.6388 | 0.6420 | |
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| 0.0019 | 18.0 | 1800 | 2.9517 | 0.67 | 0.6373 | 0.6276 | 0.6273 | |
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| 0.0013 | 19.0 | 1900 | 2.9633 | 0.67 | 0.6373 | 0.6276 | 0.6273 | |
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| 0.0007 | 20.0 | 2000 | 2.9769 | 0.675 | 0.6433 | 0.6344 | 0.6344 | |
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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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