---
language:
- zh
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- gyr66/privacy_detection
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: chinese-roberta-wwm-ext-large-lora-ner
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# chinese-roberta-wwm-ext-large-lora-ner

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.
It achieves the following results on the evaluation set:
- Loss: 0.3302
- Precision: 0.6010
- Recall: 0.7258
- F1: 0.6575
- Accuracy: 0.9106

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 28
- eval_batch_size: 56
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.729         | 1.0   | 72   | 0.3562          | 0.4495    | 0.5818 | 0.5072 | 0.8865   |
| 0.3155        | 2.0   | 144  | 0.3243          | 0.5155    | 0.6636 | 0.5803 | 0.8972   |
| 0.2561        | 3.0   | 216  | 0.3021          | 0.5599    | 0.7004 | 0.6223 | 0.9067   |
| 0.2283        | 4.0   | 288  | 0.3049          | 0.5670    | 0.6984 | 0.6259 | 0.9044   |
| 0.1952        | 5.0   | 360  | 0.3144          | 0.5836    | 0.7145 | 0.6424 | 0.9076   |
| 0.174         | 6.0   | 432  | 0.3157          | 0.5787    | 0.7183 | 0.6410 | 0.9063   |
| 0.155         | 7.0   | 504  | 0.3223          | 0.5966    | 0.7246 | 0.6544 | 0.9083   |
| 0.1436        | 8.0   | 576  | 0.3267          | 0.5921    | 0.7210 | 0.6502 | 0.9088   |
| 0.1298        | 9.0   | 648  | 0.3345          | 0.5965    | 0.7276 | 0.6556 | 0.9089   |
| 0.1226        | 10.0  | 720  | 0.3302          | 0.6010    | 0.7258 | 0.6575 | 0.9106   |


### Framework versions

- Transformers 4.27.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.2