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README.md
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---
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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
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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
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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: 0.5830
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- Precision: 0.9092
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- Recall: 0.9508
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- F1: 0.9296
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- Accuracy: 0.9462
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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: 10
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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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| 0.0277 | 1.0 | 884 | 0.5987 | 0.8694 | 0.9424 | 0.9044 | 0.9359 |
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| 0.0373 | 2.0 | 1768 | 0.5396 | 0.8824 | 0.9475 | 0.9138 | 0.9452 |
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| 0.027 | 3.0 | 2652 | 0.5509 | 0.8994 | 0.9398 | 0.9192 | 0.9459 |
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| 0.0181 | 4.0 | 3536 | 0.5706 | 0.9006 | 0.9449 | 0.9222 | 0.9434 |
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| 0.0144 | 5.0 | 4420 | 0.5605 | 0.9104 | 0.9466 | 0.9281 | 0.9462 |
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| 0.011 | 6.0 | 5304 | 0.5323 | 0.8775 | 0.9466 | 0.9107 | 0.9382 |
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| 0.0103 | 7.0 | 6188 | 0.5870 | 0.9073 | 0.9534 | 0.9298 | 0.9433 |
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| 0.0065 | 8.0 | 7072 | 0.5986 | 0.9062 | 0.95 | 0.9276 | 0.9450 |
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| 0.0098 | 9.0 | 7956 | 0.5794 | 0.9062 | 0.95 | 0.9276 | 0.9458 |
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| 0.0032 | 10.0 | 8840 | 0.5830 | 0.9092 | 0.9508 | 0.9296 | 0.9462 |
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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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