| --- |
| library_name: transformers |
| license: mit |
| base_model: BAAI/bge-small-en-v1.5 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: ner-hw2 |
| 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. --> |
|
|
| # ner-hw2 |
|
|
| This model is a fine-tuned version of [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1126 |
| - Precision: 0.9188 |
| - Recall: 0.9349 |
| - F1: 0.9268 |
| - Accuracy: 0.9836 |
|
|
| ## 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: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0767 | 1.0 | 1252 | 0.0880 | 0.8771 | 0.9042 | 0.8905 | 0.9783 | |
| | 0.0488 | 2.0 | 2504 | 0.0908 | 0.8907 | 0.9152 | 0.9028 | 0.9790 | |
| | 0.0357 | 3.0 | 3756 | 0.0889 | 0.9048 | 0.9194 | 0.9120 | 0.9805 | |
| | 0.0263 | 4.0 | 5008 | 0.0828 | 0.8966 | 0.9300 | 0.9130 | 0.9815 | |
| | 0.0176 | 5.0 | 6260 | 0.0842 | 0.9059 | 0.9280 | 0.9168 | 0.9823 | |
| | 0.0146 | 6.0 | 7512 | 0.0958 | 0.9084 | 0.9194 | 0.9139 | 0.9824 | |
| | 0.0111 | 7.0 | 8764 | 0.0993 | 0.9119 | 0.9337 | 0.9227 | 0.9829 | |
| | 0.0086 | 8.0 | 10016 | 0.1005 | 0.9137 | 0.9315 | 0.9225 | 0.9822 | |
| | 0.0063 | 9.0 | 11268 | 0.1019 | 0.9073 | 0.9307 | 0.9188 | 0.9817 | |
| | 0.0046 | 10.0 | 12520 | 0.1027 | 0.9184 | 0.9297 | 0.9240 | 0.9835 | |
| | 0.0044 | 11.0 | 13772 | 0.1092 | 0.9134 | 0.9298 | 0.9215 | 0.9830 | |
| | 0.0022 | 12.0 | 15024 | 0.1124 | 0.9139 | 0.9286 | 0.9212 | 0.9830 | |
| | 0.0025 | 13.0 | 16276 | 0.1150 | 0.9199 | 0.9334 | 0.9266 | 0.9837 | |
| | 0.0019 | 14.0 | 17528 | 0.1125 | 0.9207 | 0.9355 | 0.9280 | 0.9838 | |
| | 0.0010 | 15.0 | 18780 | 0.1126 | 0.9188 | 0.9349 | 0.9268 | 0.9836 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.19.0 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 5.1.0 |
| - Tokenizers 0.23.2 |
| |
