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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: hfl/chinese-bert-wwm-ext |
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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: ner_model_output |
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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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# ner_model_output |
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This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3016 |
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- Precision: 0.9999 |
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- Recall: 0.9999 |
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- F1: 0.9999 |
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- Accuracy: 1.0000 |
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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: 3e-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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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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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| 0.3032 | 1.0 | 6200 | 0.3026 | 0.9995 | 0.9996 | 0.9995 | 0.9997 | |
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| 0.3031 | 2.0 | 12400 | 0.3018 | 0.9999 | 0.9999 | 0.9999 | 1.0000 | |
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| 0.3025 | 3.0 | 18600 | 0.3017 | 0.9999 | 0.9999 | 0.9999 | 1.0000 | |
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| 0.3024 | 4.0 | 24800 | 0.3017 | 0.9999 | 0.9999 | 0.9999 | 1.0000 | |
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| 0.3019 | 5.0 | 31000 | 0.3016 | 0.9999 | 0.9999 | 0.9999 | 1.0000 | |
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| 0.3034 | 6.0 | 37200 | 0.3016 | 0.9999 | 0.9999 | 0.9999 | 1.0000 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.1 |
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