Create README.md

README.md

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+---
+license: "mit"
+
+widget:
+- text: "Took the pill, 12 hours later my muscles started to really hurt, then my ribs started to burn so bad I couldn't breath."
+---
+
+This model takes text (narrative of reasctions to medications) as input and returns a predicted severity score for the reaction (LABEL_1 is severe reaction). Please do NOT use for medical diagnosis.
+Example usage:
+```python
+import torch
+import tensorflow as tf
+from transformers import RobertaTokenizer, RobertaModel
+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("paragon-analytics/ADRv1")  
+model = AutoModelForSequenceClassification.from_pretrained("paragon-analytics/ADRv1")
+def adr_predict(x):
+    encoded_input = tokenizer(x, return_tensors='pt')
+    output = model(**encoded_input)
+    scores = output[0][0].detach().numpy()
+    scores = tf.nn.softmax(scores)
+    return scores.numpy()[1]
+sentence = "I have severe pain."
+
+adr_predict(sentence)
+```