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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-roman-urdu-fine-grained |
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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-roman-urdu-fine-grained |
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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: 0.7424 |
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- Accuracy: 0.7858 |
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- Precision: 0.7111 |
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- Recall: 0.6798 |
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- F1: 0.6906 |
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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: 32 |
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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: 64 |
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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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| 1.1702 | 1.0 | 113 | 1.1398 | 0.5901 | 0.3936 | 0.3331 | 0.2566 | |
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| 0.9227 | 2.0 | 226 | 0.8477 | 0.7001 | 0.2670 | 0.3508 | 0.2990 | |
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| 0.8216 | 3.0 | 339 | 0.7744 | 0.7309 | 0.3829 | 0.4267 | 0.3918 | |
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| 0.6698 | 4.0 | 452 | 0.6684 | 0.7713 | 0.5727 | 0.5493 | 0.5269 | |
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| 0.6337 | 5.0 | 565 | 0.5499 | 0.8340 | 0.6059 | 0.6291 | 0.6115 | |
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| 0.5396 | 6.0 | 678 | 0.4947 | 0.8428 | 0.6067 | 0.6571 | 0.6247 | |
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| 0.469 | 7.0 | 791 | 0.4368 | 0.8756 | 0.7950 | 0.7254 | 0.7261 | |
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| 0.4571 | 8.0 | 904 | 0.3816 | 0.9105 | 0.8661 | 0.8083 | 0.8305 | |
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| 0.4099 | 9.0 | 1017 | 0.3544 | 0.9237 | 0.8699 | 0.8494 | 0.8558 | |
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| 0.3605 | 10.0 | 1130 | 0.3385 | 0.9256 | 0.8819 | 0.8436 | 0.8576 | |
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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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