widget: - text: "I never imagined a pill could cause so much pain. It started with a dull ache, then my muscles felt like they were on fire. Breathing became a struggle, each breath felt like a battle. It's terrifying how quickly things can change when you least expect it."
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:
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("UVA-MSBA/Mod4_T12")
model = AutoModelForSequenceClassification.from_pretrained("UVA-MSBA/Mod4_T12")
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 an extremely bad headache."
adr_predict(sentence)