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license: gpl-3.0 |
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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: bert-base-chinese-finetuned-ner_0220_J_ORIDATA_FULL_NOMOD |
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results: [] |
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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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# bert-base-chinese-finetuned-ner_0220_J_ORIDATA_FULL_NOMOD |
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This model is a fine-tuned version of [ckiplab/bert-base-chinese-ner](https://huggingface.co/ckiplab/bert-base-chinese-ner) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0522 |
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- Precision: 0.9728 |
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- Recall: 0.9739 |
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- F1: 0.9733 |
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- Accuracy: 0.9954 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 12 |
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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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| 0.3616 | 1.0 | 705 | 0.0914 | 0.8789 | 0.9239 | 0.9008 | 0.9821 | |
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| 0.0643 | 2.0 | 1410 | 0.0602 | 0.9242 | 0.9420 | 0.9330 | 0.9912 | |
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| 0.0339 | 3.0 | 2115 | 0.0533 | 0.9385 | 0.9545 | 0.9465 | 0.9910 | |
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| 0.024 | 4.0 | 2820 | 0.0558 | 0.9595 | 0.9693 | 0.9644 | 0.9932 | |
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| 0.0145 | 5.0 | 3525 | 0.0584 | 0.9484 | 0.9614 | 0.9549 | 0.9921 | |
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| 0.007 | 6.0 | 4230 | 0.0535 | 0.9637 | 0.9648 | 0.9642 | 0.9940 | |
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| 0.0145 | 7.0 | 4935 | 0.0492 | 0.9573 | 0.9682 | 0.9627 | 0.9942 | |
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| 0.0091 | 8.0 | 5640 | 0.0486 | 0.9694 | 0.9716 | 0.9705 | 0.9957 | |
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| 0.0049 | 9.0 | 6345 | 0.0526 | 0.9727 | 0.9727 | 0.9727 | 0.9950 | |
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| 0.0033 | 10.0 | 7050 | 0.0515 | 0.9661 | 0.9727 | 0.9694 | 0.9949 | |
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| 0.0023 | 11.0 | 7755 | 0.0523 | 0.9661 | 0.9716 | 0.9688 | 0.9950 | |
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| 0.0019 | 12.0 | 8460 | 0.0522 | 0.9728 | 0.9739 | 0.9733 | 0.9954 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.8.0 |
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- Tokenizers 0.12.1 |
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