---
base_model: bert-base-chinese
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 [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1158
- Precision: 0.7635
- Recall: 0.7577
- F1: 0.7606
- Accuracy: 0.9626

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1101        | 1.0   | 1875 | 0.1007          | 0.7357    | 0.7458 | 0.7407 | 0.9610   |
| 0.0796        | 2.0   | 3750 | 0.1003          | 0.76      | 0.7530 | 0.7565 | 0.9627   |
| 0.0538        | 3.0   | 5625 | 0.1158          | 0.7635    | 0.7577 | 0.7606 | 0.9626   |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.18.0
- Tokenizers 0.19.1