---
base_model: gyr66/RoBERTa-ext-large-chinese-finetuned-ner
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: Ernie-3.0-large-crf-chinese-finetuned-ner
  results: []
datasets:
- gyr66/privacy_detection
language:
- zh
library_name: transformers
pipeline_tag: token-classification
---

<!-- 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. -->

# RoBERTa-ext-large-crf-chinese-finetuned-ner

This model is a fine-tuned version of [gyr66/RoBERTa-ext-large-chinese-finetuned-ner](https://huggingface.co/gyr66/RoBERTa-ext-large-chinese-finetuned-ner) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5907
- Precision: 0.7278
- Recall: 0.75
- F1: 0.7387
- Accuracy: 0.9629

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0061        | 1.0   | 503  | 0.6739          | 0.6747    | 0.7457 | 0.7084 | 0.9608   |
| 0.0078        | 2.0   | 1006 | 0.6343          | 0.7083    | 0.7518 | 0.7294 | 0.9622   |
| 0.0072        | 3.0   | 1509 | 0.6237          | 0.6867    | 0.7621 | 0.7224 | 0.9607   |
| 0.0052        | 4.0   | 2012 | 0.5929          | 0.7136    | 0.7616 | 0.7368 | 0.9635   |
| 0.0031        | 5.0   | 2515 | 0.5907          | 0.7278    | 0.75   | 0.7387 | 0.9629   |
| 0.0014        | 6.0   | 3018 | 0.6080          | 0.7172    | 0.7558 | 0.7360 | 0.9636   |
| 0.001         | 7.0   | 3521 | 0.6179          | 0.7198    | 0.7586 | 0.7387 | 0.9637   |
| 0.0005        | 8.0   | 4024 | 0.6208          | 0.7211    | 0.7518 | 0.7361 | 0.9632   |
| 0.0004        | 9.0   | 4527 | 0.6169          | 0.7271    | 0.7487 | 0.7378 | 0.9636   |
| 0.0002        | 10.0  | 5030 | 0.6202          | 0.7266    | 0.7495 | 0.7379 | 0.9636   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0