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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model_index: |
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- name: chinese-address-ner |
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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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metric: |
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name: Accuracy |
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type: accuracy |
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value: 0.9852459016393442 |
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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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# chinese-address-ner |
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This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unkown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0999 |
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- Precision: 0.9739 |
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- Recall: 0.9849 |
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- F1: 0.9794 |
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- Accuracy: 0.9852 |
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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: 50 |
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- eval_batch_size: 50 |
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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: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0656 | 0.14 | 1 | 0.1061 | 0.9665 | 0.9811 | 0.9738 | 0.9844 | |
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| 0.1305 | 0.29 | 2 | 0.1096 | 0.9630 | 0.9811 | 0.9720 | 0.9836 | |
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| 0.1009 | 0.43 | 3 | 0.0999 | 0.9739 | 0.9849 | 0.9794 | 0.9852 | |
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| 0.0844 | 0.57 | 4 | 0.0911 | 0.9739 | 0.9849 | 0.9794 | 0.9852 | |
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| 0.0773 | 0.71 | 5 | 0.0858 | 0.9703 | 0.9849 | 0.9775 | 0.9852 | |
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| 0.0997 | 0.86 | 6 | 0.0815 | 0.9739 | 0.9849 | 0.9794 | 0.9861 | |
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| 0.0904 | 1.0 | 7 | 0.0795 | 0.9739 | 0.9849 | 0.9794 | 0.9861 | |
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### Framework versions |
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- Transformers 4.8.2 |
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- Pytorch 1.7.0 |
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- Datasets 1.9.0 |
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- Tokenizers 0.10.3 |
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