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hing-roberta-CM-run-4
This model is a fine-tuned version of l3cube-pune/hing-roberta on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.5827
- Accuracy: 0.7525
- Precision: 0.6967
- Recall: 0.7004
- F1: 0.6980
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.8734 | 1.0 | 497 | 0.7673 | 0.7203 | 0.6617 | 0.6600 | 0.6604 |
| 0.6245 | 2.0 | 994 | 0.7004 | 0.7485 | 0.6951 | 0.7137 | 0.7015 |
| 0.4329 | 3.0 | 1491 | 1.0469 | 0.7223 | 0.6595 | 0.6640 | 0.6538 |
| 0.2874 | 4.0 | 1988 | 1.3103 | 0.7586 | 0.7064 | 0.7157 | 0.7104 |
| 0.1837 | 5.0 | 2485 | 1.7916 | 0.7425 | 0.6846 | 0.6880 | 0.6861 |
| 0.1121 | 6.0 | 2982 | 2.0721 | 0.7465 | 0.7064 | 0.7041 | 0.7003 |
| 0.0785 | 7.0 | 3479 | 2.3469 | 0.7425 | 0.6898 | 0.6795 | 0.6807 |
| 0.0609 | 8.0 | 3976 | 2.2775 | 0.7404 | 0.6819 | 0.6881 | 0.6845 |
| 0.0817 | 9.0 | 4473 | 2.1992 | 0.7686 | 0.7342 | 0.7147 | 0.7166 |
| 0.042 | 10.0 | 4970 | 2.2359 | 0.7565 | 0.7211 | 0.7141 | 0.7106 |
| 0.0463 | 11.0 | 5467 | 2.2291 | 0.7646 | 0.7189 | 0.7186 | 0.7177 |
| 0.027 | 12.0 | 5964 | 2.3955 | 0.7525 | 0.6994 | 0.7073 | 0.7028 |
| 0.0314 | 13.0 | 6461 | 2.4256 | 0.7565 | 0.7033 | 0.7153 | 0.7082 |
| 0.0251 | 14.0 | 6958 | 2.4578 | 0.7565 | 0.7038 | 0.7025 | 0.7027 |
| 0.0186 | 15.0 | 7455 | 2.5984 | 0.7565 | 0.7141 | 0.6945 | 0.6954 |
| 0.0107 | 16.0 | 7952 | 2.5068 | 0.7425 | 0.6859 | 0.7016 | 0.6912 |
| 0.0134 | 17.0 | 8449 | 2.5876 | 0.7606 | 0.7018 | 0.7041 | 0.7029 |
| 0.0145 | 18.0 | 8946 | 2.6011 | 0.7626 | 0.7072 | 0.7079 | 0.7073 |
| 0.0108 | 19.0 | 9443 | 2.5861 | 0.7545 | 0.6973 | 0.7017 | 0.6990 |
| 0.0076 | 20.0 | 9940 | 2.5827 | 0.7525 | 0.6967 | 0.7004 | 0.6980 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.10.1+cu111
- Datasets 2.3.2
- Tokenizers 0.12.1
