---
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