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---
language:
- zh
datasets:
- gyr66/privacy_detection
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: RoBERTa-ext-large-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: 0.67106
- name: Recall
type: recall
value: 0.75554
- name: F1
type: f1
value: 0.7108
- name: Accuracy
type: accuracy
value: 0.91212
---
# bert-base-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: 0.7408
- Precision: 0.6711
- Recall: 0.7555
- F1: 0.7108
- Accuracy: 0.9121
## Model description
The model is used for competition: "https://www.datafountain.cn/competitions/472"
## Training and evaluation data
The training and evaluation data is from [gyr66/privacy_detection](https://huggingface.co/datasets/gyr66/privacy_detection) dataset.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 28
- eval_batch_size: 28
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
### Framework versions
- Transformers 4.27.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.2
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