readme: ---
  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} 
  }
  ```