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RoBERTa-ext-lora-chinese-finetuned-ner

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RoBERTa-ext-lora-chinese-finetuned-ner / README.md
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metadata
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: []

RoBERTa-ext-lora-chinese-finetuned-ner

This model is a fine-tuned version of 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