| | --- |
| | library_name: transformers |
| | license: cc-by-4.0 |
| | base_model: l3cube-pune/indic-sentence-bert-nli |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | - precision |
| | - recall |
| | - f1 |
| | model-index: |
| | - name: indic-sentence-bert-nli-hinglish-binary |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # indic-sentence-bert-nli-hinglish-binary |
| |
|
| | 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. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.5934 |
| | - Accuracy: 0.6987 |
| | - Precision: 0.6929 |
| | - Recall: 0.6155 |
| | - F1: 0.6125 |
| |
|
| | ## 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: 5e-05 |
| | - train_batch_size: 32 |
| | - eval_batch_size: 128 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 128 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 10 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| | |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | | 0.663 | 0.9709 | 25 | 0.6551 | 0.6376 | 0.3188 | 0.5 | 0.3894 | |
| | | 0.6284 | 1.9806 | 51 | 0.6314 | 0.6676 | 0.7906 | 0.5430 | 0.4785 | |
| | | 0.6266 | 2.9903 | 77 | 0.6725 | 0.5095 | 0.6373 | 0.5975 | 0.4974 | |
| | | 0.6261 | 4.0 | 103 | 0.6008 | 0.7112 | 0.7124 | 0.6307 | 0.6311 | |
| | | 0.6202 | 4.9709 | 128 | 0.6025 | 0.7057 | 0.7005 | 0.6264 | 0.6265 | |
| | | 0.5987 | 5.9806 | 154 | 0.5907 | 0.7112 | 0.7216 | 0.6258 | 0.6236 | |
| | | 0.5856 | 6.9903 | 180 | 0.5818 | 0.7193 | 0.7253 | 0.6404 | 0.6427 | |
| | | 0.5753 | 8.0 | 206 | 0.5804 | 0.7166 | 0.7502 | 0.6252 | 0.6201 | |
| | | 0.5416 | 8.9709 | 231 | 0.5667 | 0.7221 | 0.7424 | 0.6376 | 0.6378 | |
| | | 0.5419 | 9.7087 | 250 | 0.5599 | 0.7330 | 0.7439 | 0.6575 | 0.6632 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.45.1 |
| | - Pytorch 2.4.0 |
| | - Datasets 3.0.1 |
| | - Tokenizers 0.20.0 |
| | |
