|
--- |
|
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: 0.0557 |
|
- Precision: 0.9424 |
|
- Recall: 0.9568 |
|
- F1: 0.9496 |
|
- Accuracy: 0.9890 |
|
|
|
## 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: 4 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 1.0617 | 0.1 | 100 | 0.4293 | 0.2681 | 0.2160 | 0.2393 | 0.8405 | |
|
| 0.2546 | 0.2 | 200 | 0.1427 | 0.7154 | 0.8018 | 0.7561 | 0.9523 | |
|
| 0.1644 | 0.3 | 300 | 0.1148 | 0.7712 | 0.8437 | 0.8058 | 0.9628 | |
|
| 0.132 | 0.39 | 400 | 0.0945 | 0.7956 | 0.8704 | 0.8313 | 0.9691 | |
|
| 0.107 | 0.49 | 500 | 0.0839 | 0.8425 | 0.8971 | 0.8689 | 0.9747 | |
|
| 0.0981 | 0.59 | 600 | 0.0971 | 0.8539 | 0.9060 | 0.8792 | 0.9733 | |
|
| 0.098 | 0.69 | 700 | 0.0794 | 0.8832 | 0.9034 | 0.8932 | 0.9777 | |
|
| 0.0955 | 0.79 | 800 | 0.0716 | 0.9012 | 0.9276 | 0.9142 | 0.9821 | |
|
| 0.0824 | 0.89 | 900 | 0.0697 | 0.8848 | 0.9276 | 0.9057 | 0.9789 | |
|
| 0.0774 | 0.99 | 1000 | 0.0631 | 0.8929 | 0.9212 | 0.9068 | 0.9808 | |
|
| 0.0604 | 1.09 | 1100 | 0.0701 | 0.9087 | 0.9238 | 0.9162 | 0.9812 | |
|
| 0.0621 | 1.18 | 1200 | 0.0583 | 0.9126 | 0.9288 | 0.9207 | 0.9841 | |
|
| 0.0446 | 1.28 | 1300 | 0.0652 | 0.9175 | 0.9327 | 0.9250 | 0.9839 | |
|
| 0.0516 | 1.38 | 1400 | 0.0609 | 0.9093 | 0.9301 | 0.9196 | 0.9842 | |
|
| 0.0539 | 1.48 | 1500 | 0.0648 | 0.9179 | 0.9377 | 0.9277 | 0.9858 | |
|
| 0.0546 | 1.58 | 1600 | 0.0676 | 0.9157 | 0.9390 | 0.9272 | 0.9825 | |
|
| 0.0479 | 1.68 | 1700 | 0.0574 | 0.9106 | 0.9314 | 0.9209 | 0.9848 | |
|
| 0.0424 | 1.78 | 1800 | 0.0572 | 0.9228 | 0.9416 | 0.9321 | 0.9862 | |
|
| 0.054 | 1.88 | 1900 | 0.0499 | 0.9195 | 0.9428 | 0.9310 | 0.9866 | |
|
| 0.0397 | 1.97 | 2000 | 0.0542 | 0.9318 | 0.9555 | 0.9435 | 0.9876 | |
|
| 0.0362 | 2.07 | 2100 | 0.0567 | 0.9217 | 0.9428 | 0.9322 | 0.9867 | |
|
| 0.0226 | 2.17 | 2200 | 0.0670 | 0.925 | 0.9403 | 0.9326 | 0.9854 | |
|
| 0.029 | 2.27 | 2300 | 0.0565 | 0.9375 | 0.9530 | 0.9452 | 0.9883 | |
|
| 0.0293 | 2.37 | 2400 | 0.0540 | 0.9254 | 0.9454 | 0.9353 | 0.9866 | |
|
| 0.0265 | 2.47 | 2500 | 0.0551 | 0.9304 | 0.9517 | 0.9410 | 0.9880 | |
|
| 0.0244 | 2.57 | 2600 | 0.0543 | 0.9316 | 0.9517 | 0.9415 | 0.9886 | |
|
| 0.027 | 2.67 | 2700 | 0.0500 | 0.9399 | 0.9543 | 0.9470 | 0.9894 | |
|
| 0.0286 | 2.76 | 2800 | 0.0479 | 0.9282 | 0.9530 | 0.9404 | 0.9890 | |
|
| 0.0206 | 2.86 | 2900 | 0.0549 | 0.9255 | 0.9466 | 0.9359 | 0.9880 | |
|
| 0.0239 | 2.96 | 3000 | 0.0537 | 0.9294 | 0.9530 | 0.9410 | 0.9889 | |
|
| 0.0178 | 3.06 | 3100 | 0.0557 | 0.9424 | 0.9568 | 0.9496 | 0.9890 | |
|
| 0.0131 | 3.16 | 3200 | 0.0627 | 0.9327 | 0.9504 | 0.9415 | 0.9880 | |
|
| 0.0161 | 3.26 | 3300 | 0.0586 | 0.9340 | 0.9530 | 0.9434 | 0.9883 | |
|
| 0.0162 | 3.36 | 3400 | 0.0542 | 0.9303 | 0.9504 | 0.9403 | 0.9887 | |
|
| 0.0212 | 3.46 | 3500 | 0.0562 | 0.9268 | 0.9492 | 0.9379 | 0.9881 | |
|
| 0.02 | 3.55 | 3600 | 0.0551 | 0.9280 | 0.9504 | 0.9391 | 0.9888 | |
|
| 0.0084 | 3.65 | 3700 | 0.0568 | 0.9292 | 0.9504 | 0.9397 | 0.9888 | |
|
| 0.0143 | 3.75 | 3800 | 0.0564 | 0.9363 | 0.9530 | 0.9446 | 0.9892 | |
|
| 0.0162 | 3.85 | 3900 | 0.0560 | 0.9377 | 0.9568 | 0.9472 | 0.9888 | |
|
| 0.0199 | 3.95 | 4000 | 0.0546 | 0.9377 | 0.9568 | 0.9472 | 0.9894 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.29.0.dev0 |
|
- Pytorch 1.10.1+cu113 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.3 |
|
|
