readme: ---
  tags:
  - distilbert
  - health
  - tweet
  datasets:
  - custom-phm-tweets
  metrics:
  - accuracy
  model-index:
  - name: distilbert-phmtweets-sutd
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: custom-phm-tweets
        type: labelled
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.877
  ---
  
  # distilbert-phmtweets-sutd
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 0.877
  
  ## Usage
  
  ```Python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  tokenizer = AutoTokenizer.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")
  model = AutoModelForSequenceClassification.from_pretrained("dibsondivya/distilbert-phmtweets-sutd")
  ```
  
  
  ### Model Evaluation Results
  With Validation Set
  - Accuracy: 0.8708661417322835
  
  With Test Set
  - Accuracy: 0.8772961058045555
  
  # Reference for distilbert-base-uncased Model
  ```bibtex
  @article{Sanh2019DistilBERTAD,
    title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
    author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
    journal={ArXiv},
    year={2019},
    volume={abs/1910.01108}
  }
  ```
  