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| from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer, DebertaV2Tokenizer, DebertaV2Model | |
| import sentencepiece | |
| import streamlit as st | |
| import pandas as pd | |
| import spacy | |
| example_list = [ | |
| "The primary outcome was overall survival (OS).", | |
| "Overall survival was not significantly different between the groups (hazard ratio [HR], 0.87; 95% CI, 0.66-1.16; P = .34).", | |
| "Afatinib-an oral irreversible ErbB family blocker-improves progression-free survival compared with pemetrexed and cisplatin for first-line treatment of patients with EGFR mutation-positive advanced non-small-cell lung cancer (NSCLC)." | |
| ] | |
| st.set_page_config(layout="wide") | |
| st.title("Demo for EIC NER") | |
| model_list = ['arunavsk1/my-awesome-pubmed-bert' | |
| # 'akdeniz27/convbert-base-turkish-cased-ner', | |
| # 'akdeniz27/xlm-roberta-base-turkish-ner', | |
| # 'xlm-roberta-large-finetuned-conll03-english' | |
| ] | |
| # st.sidebar.header("Select NER Model") | |
| model_checkpoint = st.sidebar.radio("", model_list) | |
| # st.sidebar.write("For details of models: 'https://huggingface.co/akdeniz27/") | |
| # st.sidebar.write("") | |
| # xlm_agg_strategy_info = "'aggregation_strategy' can be selected as 'simple' or 'none' for 'xlm-roberta' because of the RoBERTa model's tokenization approach." | |
| # st.sidebar.header("Select Aggregation Strategy Type") | |
| # if model_checkpoint == "akdeniz27/xlm-roberta-base-turkish-ner": | |
| # aggregation = st.sidebar.radio("", ('simple', 'none')) | |
| # st.sidebar.write(xlm_agg_strategy_info) | |
| # elif model_checkpoint == "xlm-roberta-large-finetuned-conll03-english": | |
| # aggregation = st.sidebar.radio("", ('simple', 'none')) | |
| # st.sidebar.write(xlm_agg_strategy_info) | |
| # st.sidebar.write("") | |
| # st.sidebar.write("This English NER model is included just to show the zero-shot transfer learning capability of XLM-Roberta.") | |
| # else: | |
| # aggregation = st.sidebar.radio("", ('first', 'simple', 'average', 'max', 'none')) | |
| # st.sidebar.write("Please refer 'https://huggingface.co/transformers/_modules/transformers/pipelines/token_classification.html' for entity grouping with aggregation_strategy parameter.") | |
| aggregation = 'none' | |
| st.subheader("Select Text Input Method") | |
| input_method = st.radio("", ('Select from Examples', 'Write or Paste New Text')) | |
| if input_method == 'Select from Examples': | |
| selected_text = st.selectbox('Select Text from List', example_list, index=0, key=1) | |
| st.subheader("Text to Run") | |
| input_text = st.text_area("Selected Text", selected_text, height=128, max_chars=None, key=2) | |
| elif input_method == "Write or Paste New Text": | |
| st.subheader("Text to Run") | |
| input_text = st.text_area('Write or Paste Text Below', value="", height=128, max_chars=None, key=2) | |
| def setModel(model_checkpoint, aggregation): | |
| model = AutoModelForTokenClassification.from_pretrained(model_checkpoint) | |
| tokenizer = AutoTokenizer.from_pretrained(model_checkpoint) | |
| return pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy=aggregation) | |
| def get_html(html: str): | |
| WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>""" | |
| html = html.replace("\n", " ") | |
| return WRAPPER.format(html) | |
| Run_Button = st.button("Run", key=None) | |
| if Run_Button == True: | |
| ner_pipeline = setModel(model_checkpoint, aggregation) | |
| output = ner_pipeline(input_text) | |
| df = pd.DataFrame.from_dict(output) | |
| if aggregation != "none": | |
| cols_to_keep = ['word','entity_group','score','start','end'] | |
| else: | |
| cols_to_keep = ['word','entity','score','start','end'] | |
| df_final = df[cols_to_keep] | |
| # st.subheader("Recognized Entities") | |
| # st.dataframe(df_final) | |
| st.subheader("Spacy Style Display") | |
| spacy_display = {} | |
| spacy_display["ents"] = [] | |
| spacy_display["text"] = input_text | |
| spacy_display["title"] = None | |
| entity_map = {'LABEL_0': 'O', | |
| 'LABEL_1': 'B-Intervention', | |
| 'LABEL_2': 'I-Intervention', | |
| 'LABEL_3': 'B-Outcome', | |
| 'LABEL_4': 'I-Outcome', | |
| 'LABEL_5': 'B-Value', | |
| 'LABEL_6': 'I-Value'} | |
| for entity in output: | |
| if aggregation != "none": | |
| spacy_display["ents"].append({"start": entity["start"], "end": entity["end"], "label": entity_map[entity["entity_group"]]}) | |
| else: | |
| spacy_display["ents"].append({"start": entity["start"], "end": entity["end"], "label": entity_map[entity["entity"]]}) | |
| entity_list = ['B-Intervention', 'I-Intervention', 'B-Outcome', 'I-Outcome', 'B-Value', 'I-Value'] | |
| colors = {'B-Intervention': '#85DCDF', | |
| 'I-Intervention': '#85DCDF', | |
| 'B-Outcome': '#DF85DC', | |
| 'I-Outcome': '#DF85DC', | |
| 'B-Value': '#DCDF85', | |
| 'I-Value': '#DCDF85'} | |
| html = spacy.displacy.render(spacy_display, style="ent", minify=True, manual=True, options={"ents": entity_list, "colors": colors}) | |
| style = "<style>mark.entity { display: inline-block }</style>" | |
| st.write(f"{style}{get_html(html)}", unsafe_allow_html=True) | |
| st.subheader("Recognized Entities") | |
| st.dataframe(df_final) |