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| import streamlit as st | |
| import torch | |
| from transformers import BertTokenizerFast | |
| from model import BertForTokenAndSequenceJointClassification | |
| def load_model(): | |
| tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased') | |
| model = BertForTokenAndSequenceJointClassification.from_pretrained( | |
| "QCRI/PropagandaTechniquesAnalysis-en-BERT", | |
| revision="v0.1.0") | |
| return tokenizer, model | |
| tokenizer, model = load_model() | |
| st.write("[Propaganda Techniques Analysis BERT](https://huggingface.co/QCRI/PropagandaTechniquesAnalysis-en-BERT) Tagger") | |
| input = st.text_area('Input', """\ | |
| In some instances, it can be highly dangerous to use a medicine for the prevention or treatment of COVID-19 that has not been approved by or has not received emergency use authorization from the FDA. | |
| """) | |
| inputs = tokenizer.encode_plus(input, return_tensors="pt") | |
| outputs = model(**inputs) | |
| sequence_class_index = torch.argmax(outputs.sequence_logits, dim=-1) | |
| sequence_class = model.sequence_tags[sequence_class_index[0]] | |
| token_class_index = torch.argmax(outputs.token_logits, dim=-1) | |
| tokens = tokenizer.convert_ids_to_tokens(inputs.input_ids[0][1:-1]) | |
| tags = [model.token_tags[i] for i in token_class_index[0].tolist()[1:-1]] | |
| st.table(list(zip(tokens, tags))) | |