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---
tags:
- ernie
- health
- tweet
datasets:
- custom-phm-tweets
metrics:
- accuracy
base_model: ernie-2.0-en
model-index:
- name: ernie-phmtweets-sutd
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: custom-phm-tweets
type: labelled
metrics:
- type: accuracy
value: 0.885
name: Accuracy
---
# 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}
}
```