metadata
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-chinese-ner
results: []
bert-base-chinese-ner
This model is a fine-tuned version of bert-base-chinese on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0378
- Precision: 0.9227
- Recall: 0.9195
- F1: 0.9211
- Accuracy: 0.9910
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0839 | 1.0 | 5796 | 0.0400 | 0.8999 | 0.8866 | 0.8932 | 0.9891 |
| 0.0266 | 2.0 | 11592 | 0.0378 | 0.9227 | 0.9195 | 0.9211 | 0.9910 |
| 0.0124 | 3.0 | 17388 | 0.0411 | 0.9361 | 0.9237 | 0.9299 | 0.9919 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2