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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-CM-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-CM-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: 2.5827 |
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- Accuracy: 0.7525 |
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- Precision: 0.6967 |
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- Recall: 0.7004 |
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- F1: 0.6980 |
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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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| 0.8734 | 1.0 | 497 | 0.7673 | 0.7203 | 0.6617 | 0.6600 | 0.6604 | |
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| 0.6245 | 2.0 | 994 | 0.7004 | 0.7485 | 0.6951 | 0.7137 | 0.7015 | |
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| 0.4329 | 3.0 | 1491 | 1.0469 | 0.7223 | 0.6595 | 0.6640 | 0.6538 | |
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| 0.2874 | 4.0 | 1988 | 1.3103 | 0.7586 | 0.7064 | 0.7157 | 0.7104 | |
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| 0.1837 | 5.0 | 2485 | 1.7916 | 0.7425 | 0.6846 | 0.6880 | 0.6861 | |
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| 0.1121 | 6.0 | 2982 | 2.0721 | 0.7465 | 0.7064 | 0.7041 | 0.7003 | |
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| 0.0785 | 7.0 | 3479 | 2.3469 | 0.7425 | 0.6898 | 0.6795 | 0.6807 | |
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| 0.0609 | 8.0 | 3976 | 2.2775 | 0.7404 | 0.6819 | 0.6881 | 0.6845 | |
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| 0.0817 | 9.0 | 4473 | 2.1992 | 0.7686 | 0.7342 | 0.7147 | 0.7166 | |
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| 0.042 | 10.0 | 4970 | 2.2359 | 0.7565 | 0.7211 | 0.7141 | 0.7106 | |
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| 0.0463 | 11.0 | 5467 | 2.2291 | 0.7646 | 0.7189 | 0.7186 | 0.7177 | |
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| 0.027 | 12.0 | 5964 | 2.3955 | 0.7525 | 0.6994 | 0.7073 | 0.7028 | |
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| 0.0314 | 13.0 | 6461 | 2.4256 | 0.7565 | 0.7033 | 0.7153 | 0.7082 | |
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| 0.0251 | 14.0 | 6958 | 2.4578 | 0.7565 | 0.7038 | 0.7025 | 0.7027 | |
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| 0.0186 | 15.0 | 7455 | 2.5984 | 0.7565 | 0.7141 | 0.6945 | 0.6954 | |
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| 0.0107 | 16.0 | 7952 | 2.5068 | 0.7425 | 0.6859 | 0.7016 | 0.6912 | |
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| 0.0134 | 17.0 | 8449 | 2.5876 | 0.7606 | 0.7018 | 0.7041 | 0.7029 | |
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| 0.0145 | 18.0 | 8946 | 2.6011 | 0.7626 | 0.7072 | 0.7079 | 0.7073 | |
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| 0.0108 | 19.0 | 9443 | 2.5861 | 0.7545 | 0.6973 | 0.7017 | 0.6990 | |
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| 0.0076 | 20.0 | 9940 | 2.5827 | 0.7525 | 0.6967 | 0.7004 | 0.6980 | |
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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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