
# ernie-phmtweets-sutd

This model is a fine-tuned version of [ernie-2.0-en](https://huggingface.co/nghuyong/ernie-2.0-en) 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: 0.885

## Usage
```Python
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

## References for ERNIE 2.0 Model
```bibtex
@article{sun2019ernie20,
  title={ERNIE 2.0: A Continual Pre-training Framework for Language Understanding},
  author={Sun, Yu and Wang, Shuohuan and Li, Yukun and Feng, Shikun and Tian, Hao and Wu, Hua and Wang, Haifeng},
  journal={arXiv preprint arXiv:1907.12412},
  year={2019} 
}
```
{"tags": ["ernie", "health", "tweet"], "datasets": ["custom-phm-tweets"], "metrics": ["accuracy"], "model-index": [{"name": "ernie-phmtweets-sutd", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "custom-phm-tweets", "type": "labelled"}, "metrics": [{"type": "accuracy", "value": 0.885, "name": "Accuracy"}]}]}]}
transformers pytorch bert text-classification ernie health tweet dataset:custom-phm-tweets arxiv:1802.09130 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #ernie #health #tweet #dataset-custom-phm-tweets #arxiv-1802.09130 #model-index #autotrain_compatible #endpoints_compatible #region-us 


# ernie-phmtweets-sutd

This model is a fine-tuned version of ernie-2.0-en 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, that worked with this custom dataset.

It achieves the following results on the evaluation set:
- Accuracy: 0.885

## Usage


### Model Evaluation Results
With Validation Set
- Accuracy: 0.889763779527559

With Test Set
- Accuracy: 0.884643644379133

## References for ERNIE 2.0 Model

# ernie-phmtweets-sutd

This model is a fine-tuned version of ernie-2.0-en 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, that worked with this custom dataset.

It achieves the following results on the evaluation set:
- Accuracy: 0.885 ## Usage ### Model Evaluation Results
With Validation Set
- Accuracy: 0.889763779527559

With Test Set
- Accuracy: 0.884643644379133 ## References for ERNIE 2.0 Model
TAGS
#transformers #pytorch #bert #text-classification #ernie #health #tweet #dataset-custom-phm-tweets #arxiv-1802.09130 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # ernie-phmtweets-sutd

This model is a fine-tuned version of ernie-2.0-en 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, that worked with this custom dataset.

It achieves the following results on the evaluation set:
- Accuracy: 0.885 ## Usage ### Model Evaluation Results
With Validation Set
- Accuracy: 0.889763779527559

With Test Set
- Accuracy: 0.884643644379133 ## References for ERNIE 2.0 Model
passage: TAGS
#transformers #pytorch #bert #text-classification #ernie #health #tweet #dataset-custom-phm-tweets #arxiv-1802.09130 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# ernie-phmtweets-sutd

This model is a fine-tuned version of ernie-2.0-en 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, that worked with this custom dataset.

It achieves the following results on the evaluation set:
- Accuracy: 0.885## Usage### Model Evaluation Results
With Validation Set
- Accuracy: 0.889763779527559

With Test Set
- Accuracy: 0.884643644379133## References for ERNIE 2.0 Model