---
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-chinese-med-ner
  results: []
license: apache-2.0
datasets:
- kaishih/CMeEE-V2
language:
- zh
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# test-ner

This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an CMeEE-V2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4423
- Precision: 0.5197
- Recall: 0.6287
- F1: 0.5690
- Accuracy: 0.8492


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6791        | 1.0   | 938  | 0.4600          | 0.5031    | 0.6096 | 0.5513 | 0.8435   |
| 0.3969        | 2.0   | 1876 | 0.4423          | 0.5197    | 0.6287 | 0.5690 | 0.8492   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1