Spaces:
Build error
Build error
| import pandas as pd | |
| from spacy import displacy | |
| from spacy.tokens import Doc | |
| from spacy.vocab import Vocab | |
| from spacy_streamlit.util import get_html | |
| import streamlit as st | |
| import torch | |
| from transformers import BertTokenizerFast | |
| from model import BertForTokenAndSequenceJointClassification, TOKEN_TAGS | |
| 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]] | |
| spaces = [not tok.startswith('##') for tok in tokens][1:] + [False] | |
| doc = Doc(Vocab(strings=set(tokens)), | |
| words=tokens, | |
| spaces=spaces, | |
| ents=[tag if tag == "O" else f"B-{tag}" for tag in tags]) | |
| labels = TOKEN_TAGS[2:] | |
| label_select = st.multiselect( | |
| "Tags", | |
| options=labels, | |
| default=labels, | |
| key=f"tags_ner_label_select", | |
| ) | |
| html = displacy.render( | |
| doc, style="ent", options={"ents": label_select, "colors": {}} | |
| ) | |
| style = "<style>mark.entity { display: inline-block }</style>" | |
| st.write(f"{style}{get_html(html)}", unsafe_allow_html=True) | |
| attrs = ["text", "label_", "start", "end", "start_char", "end_char"] | |
| data = [ | |
| [str(getattr(ent, attr)) for attr in attrs] | |
| for ent in doc.ents | |
| if ent.label_ in label_select | |
| ] | |
| if data: | |
| df = pd.DataFrame(data, columns=attrs) | |
| st.dataframe(df) | |