Create README.md

README.md

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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