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update model card README.md
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README.md
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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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model-index:
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- name: hing-roberta-NCM-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-NCM-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: 3.2053
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- Accuracy: 0.6645
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- Precision: 0.6565
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- Recall: 0.6479
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- F1: 0.6505
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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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| 0.9077 | 1.0 | 927 | 0.8070 | 0.6397 | 0.6581 | 0.6439 | 0.6382 |
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| 0.6915 | 2.0 | 1854 | 0.8635 | 0.6462 | 0.6368 | 0.6439 | 0.6357 |
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| 0.4785 | 3.0 | 2781 | 1.0961 | 0.6613 | 0.6510 | 0.6556 | 0.6505 |
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| 0.3356 | 4.0 | 3708 | 1.6867 | 0.6667 | 0.6623 | 0.6611 | 0.6595 |
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| 0.2622 | 5.0 | 4635 | 2.0271 | 0.6602 | 0.6589 | 0.6451 | 0.6482 |
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| 0.1957 | 6.0 | 5562 | 2.2565 | 0.6634 | 0.6763 | 0.6517 | 0.6541 |
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| 0.1419 | 7.0 | 6489 | 2.4627 | 0.6440 | 0.6487 | 0.6203 | 0.6230 |
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| 0.1126 | 8.0 | 7416 | 2.7844 | 0.6483 | 0.6347 | 0.6268 | 0.6295 |
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| 0.091 | 9.0 | 8343 | 2.8776 | 0.6440 | 0.6302 | 0.6315 | 0.6307 |
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| 0.0758 | 10.0 | 9270 | 3.0246 | 0.6451 | 0.6325 | 0.6227 | 0.6256 |
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| 0.0674 | 11.0 | 10197 | 2.9389 | 0.6721 | 0.6605 | 0.6501 | 0.6530 |
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| 0.0542 | 12.0 | 11124 | 3.0503 | 0.6429 | 0.6456 | 0.6315 | 0.6330 |
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| 0.0576 | 13.0 | 12051 | 3.0252 | 0.6483 | 0.6427 | 0.6435 | 0.6398 |
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| 0.0337 | 14.0 | 12978 | 3.1160 | 0.6731 | 0.6676 | 0.6545 | 0.6575 |
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| 0.0318 | 15.0 | 13905 | 3.0740 | 0.6807 | 0.6733 | 0.6647 | 0.6671 |
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| 0.0188 | 16.0 | 14832 | 3.0890 | 0.6721 | 0.6633 | 0.6574 | 0.6589 |
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| 0.0258 | 17.0 | 15759 | 3.1519 | 0.6634 | 0.6602 | 0.6456 | 0.6490 |
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| 0.017 | 18.0 | 16686 | 3.1503 | 0.6688 | 0.6638 | 0.6547 | 0.6568 |
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| 0.0146 | 19.0 | 17613 | 3.2083 | 0.6688 | 0.6621 | 0.6516 | 0.6545 |
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| 0.0125 | 20.0 | 18540 | 3.2053 | 0.6645 | 0.6565 | 0.6479 | 0.6505 |
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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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