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---
base_model: google-bert/bert-base-chinese
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- precision
- recall
- f1
model-index:
- name: NERBorder
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: generator
type: generator
config: default
split: train
args: default
metrics:
- name: Precision
type: precision
value: 0.901610712050607
- name: Recall
type: recall
value: 0.8982985303950894
- name: F1
type: f1
value: 0.8999515736949341
---
<!-- 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. -->
# NERBorder
This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5195
- Precision: 0.9016
- Recall: 0.8983
- F1: 0.9000
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.2099 | 1.0 | 416 | 0.1940 | 0.8281 | 0.8152 | 0.8216 |
| 0.1658 | 2.0 | 832 | 0.1799 | 0.8464 | 0.8590 | 0.8527 |
| 0.1276 | 3.0 | 1248 | 0.1821 | 0.8795 | 0.8639 | 0.8716 |
| 0.1076 | 4.0 | 1664 | 0.1961 | 0.8903 | 0.8788 | 0.8845 |
| 0.0792 | 5.0 | 2080 | 0.2277 | 0.8787 | 0.8869 | 0.8828 |
| 0.054 | 6.0 | 2496 | 0.2395 | 0.9084 | 0.8701 | 0.8888 |
| 0.0433 | 7.0 | 2912 | 0.2991 | 0.8999 | 0.8915 | 0.8957 |
| 0.0288 | 8.0 | 3328 | 0.3374 | 0.8919 | 0.8935 | 0.8927 |
| 0.022 | 9.0 | 3744 | 0.3752 | 0.9054 | 0.8921 | 0.8987 |
| 0.0211 | 10.0 | 4160 | 0.4105 | 0.8952 | 0.8985 | 0.8968 |
| 0.0147 | 11.0 | 4576 | 0.4084 | 0.9013 | 0.9004 | 0.9009 |
| 0.0095 | 12.0 | 4992 | 0.4542 | 0.9047 | 0.8952 | 0.8999 |
| 0.01 | 13.0 | 5408 | 0.4516 | 0.9086 | 0.8896 | 0.8990 |
| 0.0087 | 14.0 | 5824 | 0.4521 | 0.9025 | 0.8935 | 0.8980 |
| 0.0069 | 15.0 | 6240 | 0.4878 | 0.9034 | 0.9022 | 0.9028 |
| 0.0042 | 16.0 | 6656 | 0.5097 | 0.9021 | 0.8997 | 0.9009 |
| 0.006 | 17.0 | 7072 | 0.5195 | 0.9054 | 0.9008 | 0.9031 |
| 0.0043 | 18.0 | 7488 | 0.5032 | 0.9009 | 0.8977 | 0.8993 |
| 0.0029 | 19.0 | 7904 | 0.5155 | 0.9003 | 0.8962 | 0.8983 |
| 0.0034 | 20.0 | 8320 | 0.5195 | 0.9016 | 0.8983 | 0.9000 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
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