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--- |
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base_model: google-bert/bert-base-chinese |
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tags: |
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- generated_from_trainer |
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datasets: |
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- generator |
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metrics: |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: NERBorder |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: generator |
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type: generator |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.901610712050607 |
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- name: Recall |
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type: recall |
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value: 0.8982985303950894 |
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- name: F1 |
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type: f1 |
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value: 0.8999515736949341 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# NERBorder |
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This model is a fine-tuned version of [google-bert/bert-base-chinese](https://huggingface.co/google-bert/bert-base-chinese) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5195 |
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- Precision: 0.9016 |
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- Recall: 0.8983 |
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- F1: 0.9000 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| |
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| 0.2099 | 1.0 | 416 | 0.1940 | 0.8281 | 0.8152 | 0.8216 | |
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| 0.1658 | 2.0 | 832 | 0.1799 | 0.8464 | 0.8590 | 0.8527 | |
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| 0.1276 | 3.0 | 1248 | 0.1821 | 0.8795 | 0.8639 | 0.8716 | |
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| 0.1076 | 4.0 | 1664 | 0.1961 | 0.8903 | 0.8788 | 0.8845 | |
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| 0.0792 | 5.0 | 2080 | 0.2277 | 0.8787 | 0.8869 | 0.8828 | |
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| 0.054 | 6.0 | 2496 | 0.2395 | 0.9084 | 0.8701 | 0.8888 | |
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| 0.0433 | 7.0 | 2912 | 0.2991 | 0.8999 | 0.8915 | 0.8957 | |
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| 0.0288 | 8.0 | 3328 | 0.3374 | 0.8919 | 0.8935 | 0.8927 | |
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| 0.022 | 9.0 | 3744 | 0.3752 | 0.9054 | 0.8921 | 0.8987 | |
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| 0.0211 | 10.0 | 4160 | 0.4105 | 0.8952 | 0.8985 | 0.8968 | |
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| 0.0147 | 11.0 | 4576 | 0.4084 | 0.9013 | 0.9004 | 0.9009 | |
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| 0.0095 | 12.0 | 4992 | 0.4542 | 0.9047 | 0.8952 | 0.8999 | |
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| 0.01 | 13.0 | 5408 | 0.4516 | 0.9086 | 0.8896 | 0.8990 | |
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| 0.0087 | 14.0 | 5824 | 0.4521 | 0.9025 | 0.8935 | 0.8980 | |
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| 0.0069 | 15.0 | 6240 | 0.4878 | 0.9034 | 0.9022 | 0.9028 | |
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| 0.0042 | 16.0 | 6656 | 0.5097 | 0.9021 | 0.8997 | 0.9009 | |
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| 0.006 | 17.0 | 7072 | 0.5195 | 0.9054 | 0.9008 | 0.9031 | |
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| 0.0043 | 18.0 | 7488 | 0.5032 | 0.9009 | 0.8977 | 0.8993 | |
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| 0.0029 | 19.0 | 7904 | 0.5155 | 0.9003 | 0.8962 | 0.8983 | |
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| 0.0034 | 20.0 | 8320 | 0.5195 | 0.9016 | 0.8983 | 0.9000 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.0.1 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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