|
--- |
|
license: gpl-3.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: bert-base-chinese-ws-finetuned-ner_all |
|
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-ws-finetuned-ner_all |
|
|
|
This model is a fine-tuned version of [ckiplab/bert-base-chinese-ws](https://huggingface.co/ckiplab/bert-base-chinese-ws) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0330 |
|
- Precision: 0.9723 |
|
- Recall: 0.9734 |
|
- F1: 0.9728 |
|
- Accuracy: 0.9879 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 18 |
|
- eval_batch_size: 18 |
|
- 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 0.0648 | 0.29 | 500 | 0.0524 | 0.9586 | 0.9572 | 0.9579 | 0.9813 | |
|
| 0.0509 | 0.59 | 1000 | 0.0460 | 0.9615 | 0.9628 | 0.9622 | 0.9832 | |
|
| 0.0478 | 0.88 | 1500 | 0.0429 | 0.9624 | 0.9660 | 0.9642 | 0.9840 | |
|
| 0.0417 | 1.17 | 2000 | 0.0409 | 0.9650 | 0.9680 | 0.9665 | 0.9851 | |
|
| 0.0402 | 1.47 | 2500 | 0.0387 | 0.9662 | 0.9693 | 0.9677 | 0.9856 | |
|
| 0.0378 | 1.76 | 3000 | 0.0359 | 0.9699 | 0.9717 | 0.9708 | 0.9869 | |
|
| 0.0385 | 2.05 | 3500 | 0.0353 | 0.9703 | 0.9718 | 0.9710 | 0.9871 | |
|
| 0.0337 | 2.34 | 4000 | 0.0341 | 0.9709 | 0.9731 | 0.9720 | 0.9875 | |
|
| 0.0348 | 2.64 | 4500 | 0.0333 | 0.9721 | 0.9733 | 0.9727 | 0.9878 | |
|
| 0.0346 | 2.93 | 5000 | 0.0331 | 0.9722 | 0.9735 | 0.9729 | 0.9879 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.13.0 |
|
- Pytorch 1.8.0+cu111 |
|
- Datasets 2.4.0 |
|
- Tokenizers 0.10.3 |
|
|
