---
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: 0.0805
- Precision: 0.8948
- Recall: 0.9219
- F1: 0.9082
- Accuracy: 0.9813

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4814        | 1.0   | 625  | 0.1769          | 0.7653    | 0.8186 | 0.7910 | 0.9624   |
| 0.1733        | 2.0   | 1250 | 0.1109          | 0.8517    | 0.8918 | 0.8713 | 0.9759   |
| 0.1143        | 3.0   | 1875 | 0.0935          | 0.8498    | 0.9047 | 0.8764 | 0.9774   |
| 0.0675        | 4.0   | 2500 | 0.0851          | 0.8845    | 0.9142 | 0.8991 | 0.9805   |
| 0.055         | 5.0   | 3125 | 0.0822          | 0.8897    | 0.9159 | 0.9026 | 0.9813   |
| 0.0449        | 6.0   | 3750 | 0.0784          | 0.8927    | 0.9189 | 0.9056 | 0.9814   |
| 0.0417        | 7.0   | 4375 | 0.0791          | 0.8898    | 0.9187 | 0.9040 | 0.9812   |
| 0.0321        | 8.0   | 5000 | 0.0798          | 0.8993    | 0.9199 | 0.9095 | 0.9817   |
| 0.0301        | 9.0   | 5625 | 0.0805          | 0.8948    | 0.9219 | 0.9082 | 0.9813   |


### Framework versions

- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 4.1.1
- Tokenizers 0.21.2