| --- |
| 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: bert-finetuned-ner |
| 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. --> |
|
|
| # bert-finetuned-ner |
|
|
| 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: 0.0890 |
| - Precision: 0.9050 |
| - Recall: 0.9287 |
| - F1: 0.9167 |
| - Accuracy: 0.9828 |
|
|
| ## 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: 2e-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: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0647 | 1.0 | 1250 | 0.0937 | 0.8574 | 0.9124 | 0.8840 | 0.9769 | |
| | 0.0465 | 2.0 | 2500 | 0.0914 | 0.8914 | 0.9156 | 0.9033 | 0.9802 | |
| | 0.0351 | 3.0 | 3750 | 0.0871 | 0.8950 | 0.9168 | 0.9058 | 0.9814 | |
| | 0.0298 | 4.0 | 5000 | 0.0891 | 0.8966 | 0.9262 | 0.9111 | 0.9816 | |
| | 0.025 | 5.0 | 6250 | 0.0888 | 0.8962 | 0.9282 | 0.9119 | 0.9819 | |
| | 0.0193 | 6.0 | 7500 | 0.0836 | 0.9068 | 0.9291 | 0.9178 | 0.9827 | |
| | 0.0165 | 7.0 | 8750 | 0.0874 | 0.9051 | 0.9292 | 0.9170 | 0.9829 | |
| | 0.0132 | 8.0 | 10000 | 0.0890 | 0.9050 | 0.9287 | 0.9167 | 0.9828 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.56.2 |
| - Pytorch 2.8.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.1 |
| |
