import streamlit as st from datasets import load_dataset from transformers import pipeline, DistilBertForSequenceClassification, DistilBertTokenizerFast, AutoModelForSequenceClassification, AutoTokenizer, TFAutoModelForSequenceClassification # Options for models from transformers library MODEL_OPTS = ['finetuned', 'default', 'bertweet-base-sentiment-analysis', 'twitter-roberta-base', 'distilRoberta-financial-sentiment'] FINETUNED_OPT = MODEL_OPTS[0] DEFAULT_OPT = MODEL_OPTS[1] # Helper function def map_decision_to_string(example): return {'decision': decision_to_str[example['decision']]} def load_abstracts(): dataset_dict = load_dataset('HUPD/hupd', name='sample', data_files="https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather", icpr_label=None, train_filing_start_date='2016-01-01', train_filing_end_date='2016-01-31', val_filing_start_date='2016-01-01', val_filing_end_date='2016-01-01', ) abstracts = dataset_dict['train']['abstract'] dataset_dict = [] return abstracts # returns loaded model and tokenizer, if any def load_model(opt): if opt not in MODEL_OPTS: print("Incorrect model selection. Try again!") model, tokenizer = None, None # Load the chosen sentiment analysis model from transformers if opt == FINETUNED_OPT: tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased') model = DistilBertForSequenceClassification.from_pretrained('saccharinedreams/finetuned-distilbert-base-uncased-for-hupd') elif opt == DEFAULT_OPT: return model, tokenizer elif opt == 'bertweet-base-sentiment-analysis': tokenizer = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis") elif opt == 'twitter-roberta-base-sentiment': tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment") elif opt == 'distilRoberta-financial-sentiment': tokenizer = AutoTokenizer.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis") elif not model and not tokenizer: print("Model not loaded correctly. Try again!") return model, tokenizer def sentiment_analysis(model, tokenizer): if model and tokenizer: return pipeline('text-classification', model=model, tokenizer=tokenizer) else: return pipeline('text-classification') # Title the Streamlit app 'Finetuned Harvard USPTO Patent Dataset (using DistilBert-Base-Uncased)' st.title('Finetuned Harvard USPTO Patent Dataset (using DistilBert-Base-Uncased)') st.markdown('Link to the app - [sentiment-analysis-app](https://huggingface.co/spaces/saccharinedreams/sentiment-analysis-app)') abstracts = load_abstracts() print(len(abstracts)) print(abstracts[0]) dropdown_abstracts = st.selectbox('Select one of the following abstracts from the HUPD dataset:', abstracts, index=abstracts.index(abstracts[0])) model, tokenizer = load_model('finetuned') # Take in user input #user_text = st.text_input('Input text to perform sentiment analysis on here.', 'I love AI!') # The user can interact with a dropdown menu to choose a sentiment analysis model. #dropdown_value = st.selectbox('Select one of the following sentiment analysis models', MODEL_OPTS, index=MODEL_OPTS.index(DEFAULT_OPT)) #model, tokenizer = load_model(dropdown_value) # Perform sentiment analysis on the user's input result = sentiment_analysis(model, tokenizer)(dropdown_abstracts) # Display the sentiment analysis results st.markdown('Labels 0, 1: Not accepted, Accepted') st.write('Sentiment:', result[0]['label'], '; Score:', result[0]['score'])