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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-NCM-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-roberta-NCM-run-4 |
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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: 3.3405 |
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- Accuracy: 0.6505 |
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- Precision: 0.6410 |
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- Recall: 0.6318 |
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- F1: 0.6350 |
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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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| 0.8975 | 1.0 | 927 | 0.9553 | 0.6127 | 0.5994 | 0.6026 | 0.5930 | |
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| 0.6924 | 2.0 | 1854 | 0.8426 | 0.6505 | 0.6535 | 0.6344 | 0.6372 | |
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| 0.472 | 3.0 | 2781 | 1.0533 | 0.6570 | 0.6449 | 0.6442 | 0.6442 | |
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| 0.3271 | 4.0 | 3708 | 1.8111 | 0.6624 | 0.6635 | 0.6407 | 0.6448 | |
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| 0.2368 | 5.0 | 4635 | 2.1234 | 0.6483 | 0.6297 | 0.6288 | 0.6267 | |
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| 0.172 | 6.0 | 5562 | 2.5340 | 0.6419 | 0.6312 | 0.6164 | 0.6199 | |
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| 0.1251 | 7.0 | 6489 | 2.5758 | 0.6472 | 0.6405 | 0.6311 | 0.6336 | |
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| 0.0943 | 8.0 | 7416 | 2.9090 | 0.6332 | 0.6337 | 0.6090 | 0.6124 | |
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| 0.0919 | 9.0 | 8343 | 2.8236 | 0.6494 | 0.6394 | 0.6301 | 0.6329 | |
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| 0.0851 | 10.0 | 9270 | 2.9368 | 0.6570 | 0.6448 | 0.6405 | 0.6422 | |
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| 0.0602 | 11.0 | 10197 | 3.2925 | 0.6289 | 0.6221 | 0.6111 | 0.6140 | |
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| 0.0551 | 12.0 | 11124 | 3.1185 | 0.6397 | 0.6239 | 0.6108 | 0.6131 | |
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| 0.0498 | 13.0 | 12051 | 3.0170 | 0.6559 | 0.6400 | 0.6322 | 0.6341 | |
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| 0.0309 | 14.0 | 12978 | 3.0934 | 0.6537 | 0.6481 | 0.6386 | 0.6410 | |
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| 0.0303 | 15.0 | 13905 | 3.1530 | 0.6440 | 0.6292 | 0.6258 | 0.6272 | |
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| 0.028 | 16.0 | 14832 | 3.1491 | 0.6570 | 0.6502 | 0.6346 | 0.6385 | |
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| 0.0199 | 17.0 | 15759 | 3.2515 | 0.6526 | 0.6394 | 0.6295 | 0.6324 | |
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| 0.0245 | 18.0 | 16686 | 3.2644 | 0.6526 | 0.6494 | 0.6315 | 0.6356 | |
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| 0.0159 | 19.0 | 17613 | 3.3344 | 0.6483 | 0.6377 | 0.6295 | 0.6324 | |
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| 0.0116 | 20.0 | 18540 | 3.3405 | 0.6505 | 0.6410 | 0.6318 | 0.6350 | |
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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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