| --- |
| library_name: transformers |
| license: mit |
| base_model: ai4bharat/indic-bert |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: indic-bert-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-bert-hinglish-binary |
|
|
| This model is a fine-tuned version of [ai4bharat/indic-bert](https://huggingface.co/ai4bharat/indic-bert) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7521 |
| - Accuracy: 0.6681 |
| - Precision: 0.6338 |
| - Recall: 0.6182 |
| - F1: 0.6213 |
|
|
| ## 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 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.6539 | 0.9709 | 25 | 0.6510 | 0.6376 | 0.3188 | 0.5 | 0.3894 | |
| | 0.6235 | 1.9806 | 51 | 0.6296 | 0.6376 | 0.3188 | 0.5 | 0.3894 | |
| | 0.63 | 2.9903 | 77 | 0.6362 | 0.6376 | 0.3188 | 0.5 | 0.3894 | |
| | 0.6149 | 4.0 | 103 | 0.6486 | 0.6376 | 0.3188 | 0.5 | 0.3894 | |
| | 0.6088 | 4.9709 | 128 | 0.6229 | 0.6376 | 0.3188 | 0.5 | 0.3894 | |
| | 0.5572 | 5.9806 | 154 | 0.6243 | 0.6376 | 0.3188 | 0.5 | 0.3894 | |
| | 0.4985 | 6.9903 | 180 | 0.6328 | 0.6322 | 0.3178 | 0.4957 | 0.3873 | |
| | 0.4697 | 8.0 | 206 | 0.6893 | 0.6730 | 0.6504 | 0.5829 | 0.5710 | |
| | 0.4114 | 8.9709 | 231 | 0.6825 | 0.6839 | 0.6531 | 0.6288 | 0.6327 | |
| | 0.3981 | 9.7087 | 250 | 0.6905 | 0.6866 | 0.6582 | 0.6228 | 0.6258 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.45.1 |
| - Pytorch 2.4.0 |
| - Datasets 3.0.1 |
| - Tokenizers 0.20.0 |
| |
