---
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 dataset was used in an [Emory University Study on Detection of Personal Health Mentions in Social Media](https://arxiv.org/pdf/1802.09130v2.pdf), with this [custom dataset](https://github.com/emory-irlab/PHM2017).

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