metadata

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 for text classification to identify public health events through tweets. The dataset was used in an Emory University Study on Detection of Personal Health Mentions in Social Media, with this custom dataset.

It achieves the following results on the evaluation set:

Accuracy: 0.877

Usage

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