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--- |
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library_name: transformers |
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license: cc-by-4.0 |
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base_model: l3cube-pune/indic-sentence-bert-nli |
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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: indic-sentence-bert-nli-hate-mr |
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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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# indic-sentence-bert-nli-hate-mr |
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This model is a fine-tuned version of [l3cube-pune/indic-sentence-bert-nli](https://huggingface.co/l3cube-pune/indic-sentence-bert-nli) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1222 |
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- Accuracy: 0.9732 |
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- Precision: 0.9733 |
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- Recall: 0.9732 |
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- F1: 0.9732 |
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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: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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: 10 |
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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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| 0.6323 | 1.0 | 61 | 0.6216 | 0.6819 | 0.6951 | 0.6816 | 0.6762 | |
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| 0.6538 | 2.0 | 122 | 0.6064 | 0.6843 | 0.7260 | 0.6849 | 0.6695 | |
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| 0.478 | 3.0 | 183 | 0.6003 | 0.7060 | 0.7076 | 0.7059 | 0.7054 | |
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| 0.3915 | 4.0 | 244 | 0.5929 | 0.7373 | 0.748 | 0.7376 | 0.7346 | |
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| 0.3288 | 5.0 | 305 | 0.6091 | 0.7470 | 0.7560 | 0.7472 | 0.7448 | |
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| 0.2425 | 6.0 | 366 | 0.6779 | 0.7301 | 0.7344 | 0.7300 | 0.7288 | |
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| 0.1878 | 7.0 | 427 | 0.6804 | 0.7422 | 0.7423 | 0.7422 | 0.7421 | |
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| 0.1156 | 8.0 | 488 | 0.7229 | 0.7614 | 0.7616 | 0.7615 | 0.7614 | |
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| 0.1454 | 9.0 | 549 | 0.8009 | 0.7494 | 0.7528 | 0.7493 | 0.7485 | |
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| 0.1152 | 10.0 | 610 | 0.8109 | 0.7566 | 0.7571 | 0.7566 | 0.7565 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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