LABEL_0 LABEL_1 LABEL_2 LABEL_3
---
tags:
- ernie
- health
- tweet
datasets:
- custom-phm-tweets
metrics:
- accuracy
model-index:
- name: ernie-phmtweets-sutd
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: custom-phm-tweets
      type: labelled
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.885
---

# ernie-phmtweets-sutd

This model is a fine-tuned version of [ernie-2.0-en](https://huggingface.co/nghuyong/ernie-2.0-en) for text classification to identify public health events through tweets. The project was based on an [Emory University Study on Detection of Personal Health Mentions in Social Media paper](https://arxiv.org/pdf/1802.09130v2.pdf), that worked with this [custom dataset](https://github.com/emory-irlab/PHM2017).

It achieves the following results on the evaluation set:
- Accuracy: 0.885

## Usage
```Python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("dibsondivya/ernie-phmtweets-sutd")
model = AutoModelForSequenceClassification.from_pretrained("dibsondivya/ernie-phmtweets-sutd")
```

### Model Evaluation Results
With Validation Set
- Accuracy: 0.889763779527559

With Test Set
- Accuracy: 0.884643644379133

## References for ERNIE 2.0 Model
```bibtex
@article{sun2019ernie20,
  title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
  author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
  journal={arXiv preprint arXiv:1907.12412},
  year={2019} 
}
```