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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
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