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Runtime error
Runtime error
| 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) | |
| option = st.selectbox( | |
| 'Discourse Type', | |
| ('Position', 'Concluding Statement', 'Claim', 'Counterclaim' , 'Evidence', 'Lead', 'Position', 'Rebuttal')) | |
| text = st.text_area('Input Here!') | |
| if text: | |
| SEP = tokenizer1.sep_token | |
| text_edited = option + SEP + text | |
| inputs1 = tokenizer1(text_edited , 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.text(out) |