metadata
base_model: bert-base-chinese
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results: []
bert-finetuned-ner
This model is a fine-tuned version of 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