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---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa-ext-lora-chinese-finetuned-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. -->
# RoBERTa-ext-lora-chinese-finetuned-ner
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co./hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6506
- Precision: 0.6463
- Recall: 0.7339
- F1: 0.6873
- Accuracy: 0.9081
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.6585 | 1.0 | 126 | 0.3495 | 0.4949 | 0.6045 | 0.5443 | 0.8916 |
| 0.3145 | 2.0 | 252 | 0.3116 | 0.5286 | 0.6644 | 0.5888 | 0.9000 |
| 0.2644 | 3.0 | 378 | 0.3112 | 0.5425 | 0.7012 | 0.6117 | 0.9029 |
| 0.2373 | 4.0 | 504 | 0.3028 | 0.5696 | 0.7090 | 0.6317 | 0.9058 |
| 0.2078 | 5.0 | 630 | 0.3141 | 0.5933 | 0.7102 | 0.6465 | 0.9059 |
| 0.192 | 6.0 | 756 | 0.3091 | 0.5842 | 0.7037 | 0.6384 | 0.9069 |
| 0.1708 | 7.0 | 882 | 0.3224 | 0.5803 | 0.7165 | 0.6413 | 0.9046 |
| 0.1557 | 8.0 | 1008 | 0.3306 | 0.6088 | 0.6833 | 0.6439 | 0.9067 |
| 0.1424 | 9.0 | 1134 | 0.3243 | 0.6031 | 0.6961 | 0.6463 | 0.9089 |
| 0.1285 | 10.0 | 1260 | 0.3451 | 0.6041 | 0.7142 | 0.6546 | 0.9064 |
| 0.1223 | 11.0 | 1386 | 0.3578 | 0.6016 | 0.7080 | 0.6505 | 0.9054 |
| 0.1111 | 12.0 | 1512 | 0.3615 | 0.6167 | 0.7158 | 0.6625 | 0.9080 |
| 0.1025 | 13.0 | 1638 | 0.3918 | 0.6073 | 0.7163 | 0.6573 | 0.9051 |
| 0.093 | 14.0 | 1764 | 0.3957 | 0.6119 | 0.7329 | 0.6670 | 0.9078 |
| 0.0858 | 15.0 | 1890 | 0.4050 | 0.6179 | 0.7052 | 0.6587 | 0.9067 |
| 0.0769 | 16.0 | 2016 | 0.4218 | 0.6178 | 0.7170 | 0.6637 | 0.9067 |
| 0.0716 | 17.0 | 2142 | 0.4213 | 0.6090 | 0.7223 | 0.6608 | 0.9057 |
| 0.0657 | 18.0 | 2268 | 0.4486 | 0.6028 | 0.7299 | 0.6603 | 0.9048 |
| 0.0652 | 19.0 | 2394 | 0.4388 | 0.6301 | 0.7193 | 0.6718 | 0.9058 |
| 0.0653 | 20.0 | 2520 | 0.4563 | 0.6165 | 0.7002 | 0.6557 | 0.9039 |
| 0.0568 | 21.0 | 2646 | 0.4549 | 0.6057 | 0.7283 | 0.6614 | 0.9034 |
| 0.0522 | 22.0 | 2772 | 0.4711 | 0.6216 | 0.7205 | 0.6674 | 0.9066 |
| 0.0486 | 23.0 | 2898 | 0.4995 | 0.6267 | 0.7148 | 0.6678 | 0.9062 |
| 0.0477 | 24.0 | 3024 | 0.4938 | 0.6228 | 0.7261 | 0.6705 | 0.9056 |
| 0.0415 | 25.0 | 3150 | 0.5129 | 0.6365 | 0.7180 | 0.6748 | 0.9075 |
| 0.0404 | 26.0 | 3276 | 0.5096 | 0.6287 | 0.7258 | 0.6738 | 0.9072 |
| 0.0364 | 27.0 | 3402 | 0.5331 | 0.6390 | 0.7205 | 0.6773 | 0.9058 |
| 0.0362 | 28.0 | 3528 | 0.5572 | 0.6317 | 0.7263 | 0.6757 | 0.9061 |
| 0.0331 | 29.0 | 3654 | 0.5603 | 0.6377 | 0.7228 | 0.6776 | 0.9050 |
| 0.0316 | 30.0 | 3780 | 0.5588 | 0.6304 | 0.7256 | 0.6746 | 0.9050 |
| 0.0321 | 31.0 | 3906 | 0.5579 | 0.6366 | 0.7190 | 0.6753 | 0.9067 |
| 0.0283 | 32.0 | 4032 | 0.5785 | 0.6469 | 0.7163 | 0.6798 | 0.9067 |
| 0.0284 | 33.0 | 4158 | 0.5698 | 0.6357 | 0.7246 | 0.6773 | 0.9073 |
| 0.0256 | 34.0 | 4284 | 0.5816 | 0.6333 | 0.7314 | 0.6788 | 0.9066 |
| 0.0231 | 35.0 | 4410 | 0.6032 | 0.6273 | 0.7276 | 0.6737 | 0.9059 |
| 0.0223 | 36.0 | 4536 | 0.6044 | 0.6317 | 0.7238 | 0.6746 | 0.9067 |
| 0.0225 | 37.0 | 4662 | 0.6007 | 0.6243 | 0.7246 | 0.6707 | 0.9060 |
| 0.0209 | 38.0 | 4788 | 0.6072 | 0.6325 | 0.7213 | 0.6740 | 0.9067 |
| 0.0199 | 39.0 | 4914 | 0.6145 | 0.6379 | 0.7261 | 0.6791 | 0.9075 |
| 0.018 | 40.0 | 5040 | 0.6299 | 0.6412 | 0.7341 | 0.6845 | 0.9070 |
| 0.0174 | 41.0 | 5166 | 0.6264 | 0.6448 | 0.7299 | 0.6847 | 0.9069 |
| 0.0172 | 42.0 | 5292 | 0.6389 | 0.6409 | 0.7369 | 0.6856 | 0.9061 |
| 0.0155 | 43.0 | 5418 | 0.6489 | 0.6381 | 0.7241 | 0.6784 | 0.9071 |
| 0.0156 | 44.0 | 5544 | 0.6418 | 0.6384 | 0.7339 | 0.6828 | 0.9065 |
| 0.0143 | 45.0 | 5670 | 0.6503 | 0.6378 | 0.7243 | 0.6783 | 0.9066 |
| 0.0149 | 46.0 | 5796 | 0.6506 | 0.6463 | 0.7339 | 0.6873 | 0.9081 |
| 0.0135 | 47.0 | 5922 | 0.6497 | 0.6432 | 0.7294 | 0.6836 | 0.9072 |
| 0.0135 | 48.0 | 6048 | 0.6563 | 0.6389 | 0.7256 | 0.6795 | 0.9069 |
| 0.013 | 49.0 | 6174 | 0.6599 | 0.6377 | 0.7306 | 0.6810 | 0.9066 |
| 0.0125 | 50.0 | 6300 | 0.6582 | 0.6380 | 0.7278 | 0.6800 | 0.9070 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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