---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-chinese-david-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-base-chinese-david-ner

This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0677
- Precision: 0.8954
- Recall: 0.8935
- F1: 0.8945
- Accuracy: 0.9830

## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1093        | 0.14  | 50   | 0.5193          | 0.16      | 0.1304 | 0.1437 | 0.8199   |
| 0.3453        | 0.28  | 100  | 0.1877          | 0.5811    | 0.6696 | 0.6222 | 0.9390   |
| 0.2117        | 0.42  | 150  | 0.1344          | 0.6907    | 0.7087 | 0.6996 | 0.9526   |
| 0.193         | 0.56  | 200  | 0.1159          | 0.7228    | 0.7370 | 0.7298 | 0.9593   |
| 0.1625        | 0.7   | 250  | 0.1191          | 0.7367    | 0.7543 | 0.7454 | 0.9603   |
| 0.1302        | 0.84  | 300  | 0.1448          | 0.7332    | 0.7587 | 0.7457 | 0.9550   |
| 0.1396        | 0.98  | 350  | 0.0899          | 0.8226    | 0.8370 | 0.8297 | 0.9720   |
| 0.0966        | 1.12  | 400  | 0.0918          | 0.8240    | 0.8348 | 0.8294 | 0.9732   |
| 0.1077        | 1.26  | 450  | 0.0824          | 0.7944    | 0.8565 | 0.8243 | 0.9742   |
| 0.0895        | 1.4   | 500  | 0.0793          | 0.8121    | 0.8457 | 0.8285 | 0.9761   |
| 0.0968        | 1.54  | 550  | 0.0797          | 0.8409    | 0.85   | 0.8454 | 0.9773   |
| 0.1172        | 1.68  | 600  | 0.0694          | 0.8422    | 0.8587 | 0.8504 | 0.9792   |
| 0.0974        | 1.82  | 650  | 0.0710          | 0.8354    | 0.8609 | 0.8480 | 0.9780   |
| 0.0941        | 1.96  | 700  | 0.0650          | 0.8543    | 0.8543 | 0.8543 | 0.9804   |
| 0.0755        | 2.09  | 750  | 0.0673          | 0.8789    | 0.8674 | 0.8731 | 0.9816   |
| 0.0559        | 2.23  | 800  | 0.0744          | 0.8544    | 0.8674 | 0.8608 | 0.9792   |
| 0.0689        | 2.37  | 850  | 0.0707          | 0.8596    | 0.8652 | 0.8624 | 0.9799   |
| 0.0525        | 2.51  | 900  | 0.0677          | 0.8954    | 0.8935 | 0.8945 | 0.9830   |
| 0.0631        | 2.65  | 950  | 0.0646          | 0.8886    | 0.8848 | 0.8867 | 0.9830   |
| 0.0699        | 2.79  | 1000 | 0.0630          | 0.8932    | 0.8913 | 0.8923 | 0.9840   |
| 0.053         | 2.93  | 1050 | 0.0636          | 0.8950    | 0.8891 | 0.8920 | 0.9837   |


### Framework versions

- Transformers 4.29.0.dev0
- Pytorch 1.10.1+cu113
- Datasets 2.11.0
- Tokenizers 0.13.3