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import logging |
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import os |
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import psutil |
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import streamlit as st |
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from PIL import Image |
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from streamlit import components |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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from transformers_interpret import SequenceClassificationExplainer |
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os.environ["TOKENIZERS_PARALLELISM"] = "false" |
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logging.basicConfig( |
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format="%(asctime)s : %(levelname)s : %(message)s", level=logging.INFO |
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) |
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def print_memory_usage(): |
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logging.info(f"RAM memory % used: {psutil.virtual_memory()[2]}") |
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@st.cache(allow_output_mutation=True, suppress_st_warning=True, max_entries=1) |
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def load_model(model_name): |
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return ( |
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AutoModelForSequenceClassification.from_pretrained(model_name), |
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AutoTokenizer.from_pretrained(model_name), |
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) |
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def main(): |
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st.title("Transformers Interpet Demo App") |
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image = Image.open("./images/tight@1920x_transparent.png") |
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st.sidebar.image(image, use_column_width=True) |
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st.sidebar.markdown( |
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"Check out the package on [Github](https://github.com/cdpierse/transformers-interpret)" |
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) |
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st.info( |
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"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. " |
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) |
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models = { |
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"distilbert-base-uncased-finetuned-sst-2-english": "DistilBERT model finetuned on SST-2 sentiment analysis task. Predicts positive/negative sentiment.", |
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"sampathkethineedi/industry-classification": "DistilBERT Model to classify a business description into one of 62 industry tags.", |
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"MoritzLaurer/policy-distilbert-7d": "DistilBERT model finetuned to classify text into one of seven political categories.", |
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} |
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model_name = st.sidebar.selectbox( |
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"Choose a classification model", list(models.keys()) |
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) |
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model, tokenizer = load_model(model_name) |
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if model_name.startswith("textattack/"): |
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model.config.id2label = {0: "NEGATIVE (0) ", 1: "POSITIVE (1)"} |
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model.eval() |
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cls_explainer = SequenceClassificationExplainer(model=model, tokenizer=tokenizer) |
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if cls_explainer.accepts_position_ids: |
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emb_type_name = st.sidebar.selectbox( |
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"Choose embedding type for attribution.", ["word", "position"] |
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) |
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if emb_type_name == "word": |
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emb_type_num = 0 |
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if emb_type_name == "position": |
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emb_type_num = 1 |
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else: |
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emb_type_num = 0 |
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explanation_classes = ["predicted"] + list(model.config.label2id.keys()) |
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explanation_class_choice = st.sidebar.selectbox( |
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"Explanation class: The class you would like to explain output with respect to.", |
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explanation_classes, |
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) |
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my_expander = st.beta_expander( |
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"Click here for description of models and their tasks" |
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) |
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with my_expander: |
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st.json(models) |
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text = st.text_area( |
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"Enter text to be interpreted", |
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"I like you, I love you", |
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height=400, |
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max_chars=850, |
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) |
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if st.button("Interpret Text"): |
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print_memory_usage() |
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st.text("Output") |
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with st.spinner("Interpreting your text (This may take some time)"): |
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if explanation_class_choice != "predicted": |
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word_attributions = cls_explainer( |
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text, |
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class_name=explanation_class_choice, |
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embedding_type=emb_type_num, |
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internal_batch_size=2, |
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) |
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else: |
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word_attributions = cls_explainer( |
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text, embedding_type=emb_type_num, internal_batch_size=2 |
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) |
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if word_attributions: |
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word_attributions_expander = st.beta_expander( |
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"Click here for raw word attributions" |
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) |
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with word_attributions_expander: |
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st.json(word_attributions) |
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components.v1.html( |
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cls_explainer.visualize()._repr_html_(), scrolling=True, height=350 |
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) |
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if __name__ == "__main__": |
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main() |
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