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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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