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--- |
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tags: |
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- ernie |
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- health |
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- tweet |
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datasets: |
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- custom-phm-tweets |
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metrics: |
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- accuracy |
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model-index: |
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- name: ernie-phmtweets-sutd |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: custom-phm-tweets |
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type: labelled |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.885 |
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--- |
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# distilbert-phmtweets-sutd |
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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). |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.885 |
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## Usage |
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`from transformers import AutoTokenizer, AutoModelForSequenceClassification` |
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`tokenizer = AutoTokenizer.from_pretrained("dibsondivya/ernie-phmtweets-sutd")` |
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`model = AutoModelForSequenceClassification.from_pretrained("dibsondivya/ernie-phmtweets-sutd")` |
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### Model Evaluation Results |
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With Validation Set |
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- Accuracy: 0.889763779527559 |
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With Test Set |
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- Accuracy: 0.884643644379133 |
