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import streamlit as st | |
import torch | |
import numpy as np | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
tokenizer1 = AutoTokenizer.from_pretrained('kitkeat/distilbert-based-uncased-argumentativewriting') | |
tokenizer2 = AutoTokenizer.from_pretrained('kitkeat/bert-large-uncased-sparse-90-unstructured-pruneofa-argumentativewriting') | |
tokenizer3 = AutoTokenizer.from_pretrained('kitkeat/deberta-v3-base-argumentativewriting') | |
model1 = AutoModelForSequenceClassification.from_pretrained('kitkeat/distilbert-based-uncased-argumentativewriting',num_labels=3) | |
model2 = AutoModelForSequenceClassification.from_pretrained('kitkeat/bert-large-uncased-sparse-90-unstructured-pruneofa-argumentativewriting',num_labels=3) | |
model3 = AutoModelForSequenceClassification.from_pretrained('kitkeat/deberta-v3-base-argumentativewriting',num_labels=3) | |
text = st.text_area('Input Here!') | |
if text: | |
inputs1 = tokenizer1(text, padding=True, truncation=True, return_tensors="pt") | |
inputs2 = tokenizer2(text, padding=True, truncation=True, return_tensors="pt") | |
inputs3 = tokenizer3(text, padding=True, truncation=True, return_tensors="pt") | |
outputs1 = model1(**inputs1) | |
outputs2 = model2(**inputs2) | |
outputs3 = model3(**inputs3) | |
prediction = outputs1.logits.argmax(dim=-1).item() | |
# model.config.id2label | |
if prediction == 0: | |
out = 'Adequate' | |
elif prediction == 1: | |
out = 'Effective' | |
elif prediction == 2: | |
out = 'Ineffective' | |
st.json(out) |