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| import asyncio | |
| import gc | |
| import logging | |
| import os | |
| import pandas as pd | |
| import psutil | |
| import streamlit as st | |
| from PIL import Image | |
| from streamlit import components | |
| from streamlit.caching import clear_cache | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| from transformers_interpret import SequenceClassificationExplainer | |
| os.environ["TOKENIZERS_PARALLELISM"] = "false" | |
| logging.basicConfig( | |
| format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO | |
| ) | |
| def print_memory_usage(): | |
| logging.info(f"RAM memory % used: {psutil.virtual_memory()[2]}") | |
| def load_model(model_name): | |
| return ( | |
| AutoModelForSequenceClassification.from_pretrained(model_name), | |
| AutoTokenizer.from_pretrained(model_name), | |
| ) | |
| def main(): | |
| st.title("Transformers Interpet Demo App") | |
| image = Image.open("./images/tight@1920x_transparent.png") | |
| st.sidebar.image(image, use_column_width=True) | |
| st.sidebar.markdown( | |
| "Check out the package on [Github](https://github.com/cdpierse/transformers-interpret)" | |
| ) | |
| st.info( | |
| "Due to limited resources only low memory models are available. Run this [app locally](https://github.com/cdpierse/transformers-interpret-streamlit) to run the full selection of available models. " | |
| ) | |
| # uncomment the options below to test out the app with a variety of classification models. | |
| models = { | |
| # "textattack/distilbert-base-uncased-rotten-tomatoes": "", | |
| # "textattack/bert-base-uncased-rotten-tomatoes": "", | |
| # "textattack/roberta-base-rotten-tomatoes": "", | |
| # "mrm8488/bert-mini-finetuned-age_news-classification": "BERT-Mini finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.", | |
| # "nateraw/bert-base-uncased-ag-news": "BERT finetuned on AG News dataset. Predicts news class (sports/tech/business/world) of text.", | |
| "distilbert-base-uncased-finetuned-sst-2-english": "DistilBERT model finetuned on SST-2 sentiment analysis task. Predicts positive/negative sentiment.", | |
| # "ProsusAI/finbert": "BERT model finetuned to predict sentiment of financial text. Finetuned on Financial PhraseBank data. Predicts positive/negative/neutral.", | |
| "sampathkethineedi/industry-classification": "DistilBERT Model to classify a business description into one of 62 industry tags.", | |
| "MoritzLaurer/policy-distilbert-7d": "DistilBERT model finetuned to classify text into one of seven political categories.", | |
| # # "MoritzLaurer/covid-policy-roberta-21": "(Under active development ) RoBERTA model finetuned to identify COVID policy measure classes ", | |
| # "mrm8488/bert-tiny-finetuned-sms-spam-detection": "Tiny bert model finetuned for spam detection. 0 == not spam, 1 == spam", | |
| } | |
| model_name = st.sidebar.selectbox( | |
| "Choose a classification model", list(models.keys()) | |
| ) | |
| model, tokenizer = load_model(model_name) | |
| if model_name.startswith("textattack/"): | |
| model.config.id2label = {0: "NEGATIVE (0) ", 1: "POSITIVE (1)"} | |
| model.eval() | |
| cls_explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer) | |
| if cls_explainer.accepts_position_ids: | |
| emb_type_name = st.sidebar.selectbox( | |
| "Choose embedding type for attribution.", ["word", "position"] | |
| ) | |
| if emb_type_name == "word": | |
| emb_type_num = 0 | |
| if emb_type_name == "position": | |
| emb_type_num = 1 | |
| else: | |
| emb_type_num = 0 | |
| explanation_classes = ["predicted"] + list(model.config.label2id.keys()) | |
| explanation_class_choice = st.sidebar.selectbox( | |
| "Explanation class: The class you would like to explain output with respect to.", | |
| explanation_classes, | |
| ) | |
| my_expander = st.beta_expander( | |
| "Click here for description of models and their tasks" | |
| ) | |
| with my_expander: | |
| st.json(models) | |
| # st.info("Max char limit of 350 (memory management)") | |
| text = st.text_area( | |
| "Enter text to be interpreted", | |
| "I like you, I love you", | |
| height=400, | |
| max_chars=850, | |
| ) | |
| if st.button("Interpret Text"): | |
| print_memory_usage() | |
| st.text("Output") | |
| with st.spinner("Interpreting your text (This may take some time)"): | |
| if explanation_class_choice != "predicted": | |
| word_attributions = cls_explainer( | |
| text, | |
| class_name=explanation_class_choice, | |
| embedding_type=emb_type_num, | |
| internal_batch_size=2, | |
| ) | |
| else: | |
| word_attributions = cls_explainer( | |
| text, embedding_type=emb_type_num, internal_batch_size=2 | |
| ) | |
| if word_attributions: | |
| word_attributions_expander = st.beta_expander( | |
| "Click here for raw word attributions" | |
| ) | |
| with word_attributions_expander: | |
| st.json(word_attributions) | |
| components.v1.html( | |
| cls_explainer.visualize()._repr_html_(), scrolling=True, height=350 | |
| ) | |
| if __name__ == "__main__": | |
| main() | |