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--- |
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language: |
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- zh |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- gyr66/privacy_detection |
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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-roberta-wwm-ext-large-lora-ner |
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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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# chinese-roberta-wwm-ext-large-lora-ner |
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This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) on the gyr66/privacy_detection dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3302 |
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- Precision: 0.6010 |
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- Recall: 0.7258 |
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- F1: 0.6575 |
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- Accuracy: 0.9106 |
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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: 0.001 |
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- train_batch_size: 28 |
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- eval_batch_size: 56 |
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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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| 0.729 | 1.0 | 72 | 0.3562 | 0.4495 | 0.5818 | 0.5072 | 0.8865 | |
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| 0.3155 | 2.0 | 144 | 0.3243 | 0.5155 | 0.6636 | 0.5803 | 0.8972 | |
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| 0.2561 | 3.0 | 216 | 0.3021 | 0.5599 | 0.7004 | 0.6223 | 0.9067 | |
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| 0.2283 | 4.0 | 288 | 0.3049 | 0.5670 | 0.6984 | 0.6259 | 0.9044 | |
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| 0.1952 | 5.0 | 360 | 0.3144 | 0.5836 | 0.7145 | 0.6424 | 0.9076 | |
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| 0.174 | 6.0 | 432 | 0.3157 | 0.5787 | 0.7183 | 0.6410 | 0.9063 | |
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| 0.155 | 7.0 | 504 | 0.3223 | 0.5966 | 0.7246 | 0.6544 | 0.9083 | |
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| 0.1436 | 8.0 | 576 | 0.3267 | 0.5921 | 0.7210 | 0.6502 | 0.9088 | |
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| 0.1298 | 9.0 | 648 | 0.3345 | 0.5965 | 0.7276 | 0.6556 | 0.9089 | |
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| 0.1226 | 10.0 | 720 | 0.3302 | 0.6010 | 0.7258 | 0.6575 | 0.9106 | |
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### Framework versions |
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- Transformers 4.27.3 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.2 |
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