
# 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 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: 0.877

## Usage

```Python
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

# Reference for distilbert-base-uncased Model
```bibtex
@article{Sanh2019DistilBERTAD,
  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
  author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.01108}
}
```

{"tags": ["distilbert", "health", "tweet"], "datasets": ["custom-phm-tweets"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-phmtweets-sutd", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "custom-phm-tweets", "type": "labelled"}, "metrics": [{"type": "accuracy", "value": 0.877, "name": "Accuracy"}]}]}]}
transformers pytorch distilbert text-classification health tweet dataset:custom-phm-tweets arxiv:1802.09130 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #health #tweet #dataset-custom-phm-tweets #arxiv-1802.09130 #model-index #autotrain_compatible #endpoints_compatible #region-us 


# 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 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: 0.877

## Usage




### Model Evaluation Results
With Validation Set
- Accuracy: 0.8708661417322835

With Test Set
- Accuracy: 0.8772961058045555

# Reference for distilbert-base-uncased Model


# 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 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: 0.877 ## Usage ### Model Evaluation Results
With Validation Set
- Accuracy: 0.8708661417322835

With Test Set
- Accuracy: 0.8772961058045555 # Reference for distilbert-base-uncased Model
TAGS
#transformers #pytorch #distilbert #text-classification #health #tweet #dataset-custom-phm-tweets #arxiv-1802.09130 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # 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 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: 0.877 ## Usage ### Model Evaluation Results
With Validation Set
- Accuracy: 0.8708661417322835

With Test Set
- Accuracy: 0.8772961058045555 # Reference for distilbert-base-uncased Model
passage: TAGS
#transformers #pytorch #distilbert #text-classification #health #tweet #dataset-custom-phm-tweets #arxiv-1802.09130 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# 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 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: 0.877## Usage### Model Evaluation Results
With Validation Set
- Accuracy: 0.8708661417322835

With Test Set
- Accuracy: 0.8772961058045555# Reference for distilbert-base-uncased Model