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update model card README.md
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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_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: 0.3786
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- Precision: 0.9357
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- Recall: 0.9657
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- F1: 0.9504
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- Accuracy: 0.9577
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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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| 0.0925 | 1.0 | 5358 | 0.2337 | 0.9246 | 0.9655 | 0.9446 | 0.9554 |
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| 0.0787 | 2.0 | 10716 | 0.2506 | 0.9208 | 0.9588 | 0.9394 | 0.9525 |
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| 0.0606 | 3.0 | 16074 | 0.2914 | 0.9309 | 0.9621 | 0.9462 | 0.9537 |
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| 0.0543 | 4.0 | 21432 | 0.2792 | 0.9248 | 0.9633 | 0.9437 | 0.9553 |
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| 0.056 | 5.0 | 26790 | 0.3064 | 0.9332 | 0.9645 | 0.9486 | 0.9563 |
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| 0.0384 | 6.0 | 32148 | 0.3317 | 0.9347 | 0.9632 | 0.9487 | 0.9564 |
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| 0.0265 | 7.0 | 37506 | 0.3340 | 0.9342 | 0.9667 | 0.9502 | 0.9568 |
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| 0.03 | 8.0 | 42864 | 0.3460 | 0.9363 | 0.9641 | 0.9500 | 0.9558 |
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| 0.0192 | 9.0 | 48222 | 0.3649 | 0.9357 | 0.9651 | 0.9501 | 0.9576 |
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| 0.0117 | 10.0 | 53580 | 0.3786 | 0.9357 | 0.9657 | 0.9504 | 0.9577 |
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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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