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---
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
---
# 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: 0.885
## Usage
`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 |