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--- |
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license: "mit" |
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widget: |
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- 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." |
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--- |
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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. |
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Example usage: |
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```python |
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import torch |
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import tensorflow as tf |
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from transformers import RobertaTokenizer, RobertaModel |
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from transformers import AutoModelForSequenceClassification |
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from transformers import TFAutoModelForSequenceClassification |
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from transformers import AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("UVA-MSBA/Mod4_T7") |
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model = AutoModelForSequenceClassification.from_pretrained("UVA-MSBA/Mod4_T7") |
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def adr_predict(x): |
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encoded_input = tokenizer(x, return_tensors='pt') |
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output = model(**encoded_input) |
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scores = output[0][0].detach().numpy() |
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scores = tf.nn.softmax(scores) |
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return scores.numpy()[1] |
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sentence = "I have severe pain." |
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adr_predict(sentence) |
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``` |
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