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---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
datasets:
- gyr66/privacy_detection
language:
- zh
model-index:
- name: RoBERTa-ext-large-crf-chinese-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: gyr66/privacy_detection
type: gyr66/privacy_detection
config: privacy_detection
split: train
args: privacy_detection
metrics:
- name: Precision
type: precision
value: 0.6813
- name: Recall
type: recall
value: 0.7573
- name: F1
type: f1
value: 0.7173
- name: Accuracy
type: accuracy
value: 0.9639
---
# RoBERTa-ext-large-crf-chinese-finetuned-ner
This model is a fine-tuned version of [chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) on the [gyr66/privacy_detection](https://huggingface.co/datasets/gyr66/privacy_detection) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7186
- Precision: 0.6813
- Recall: 0.7573
- F1: 0.7173
- Accuracy: 0.9639
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0197 | 1.0 | 503 | 0.6375 | 0.6663 | 0.7314 | 0.6973 | 0.9621 |
| 0.0251 | 2.0 | 1006 | 0.6048 | 0.6494 | 0.7435 | 0.6933 | 0.9611 |
| 0.0176 | 3.0 | 1509 | 0.6196 | 0.6669 | 0.7389 | 0.7011 | 0.9618 |
| 0.0116 | 4.0 | 2012 | 0.6361 | 0.6511 | 0.7560 | 0.6997 | 0.9624 |
| 0.0082 | 5.0 | 2515 | 0.6682 | 0.6746 | 0.7387 | 0.7052 | 0.9622 |
| 0.0067 | 6.0 | 3018 | 0.6587 | 0.6715 | 0.7409 | 0.7045 | 0.9635 |
| 0.0046 | 7.0 | 3521 | 0.6846 | 0.6770 | 0.7613 | 0.7167 | 0.9636 |
| 0.0019 | 8.0 | 4024 | 0.7081 | 0.6766 | 0.7510 | 0.7118 | 0.9630 |
| 0.0014 | 9.0 | 4527 | 0.7064 | 0.6812 | 0.7553 | 0.7163 | 0.9641 |
| 0.001 | 10.0 | 5030 | 0.7186 | 0.6813 | 0.7573 | 0.7173 | 0.9639 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0 |