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