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| import subprocess | |
| import sys | |
| package = "./shap-0.40.0-cp38-cp38-linux_x86_64.whl" | |
| subprocess.check_call( | |
| [sys.executable, "-m", "pip", "install", package] | |
| ) | |
| import numpy as np | |
| import pandas as pd | |
| import shap | |
| import streamlit as st | |
| import streamlit.components.v1 as components | |
| import torch | |
| from datasets import load_dataset | |
| from transformers import (AutoModelForCausalLM, AutoModelForQuestionAnswering, | |
| AutoModelForSeq2SeqLM, | |
| AutoModelForSequenceClassification, AutoTokenizer, | |
| pipeline) | |
| st.set_page_config(page_title="HF-SHAP") | |
| st.title("HF-SHAP: A front end for SHAP") | |
| st.caption("By Allen Roush") | |
| st.caption("github: https://github.com/Hellisotherpeople") | |
| st.caption("Linkedin: https://www.linkedin.com/in/allen-roush-27721011b/") | |
| st.title("SHAP (SHapley Additive exPlanations)") | |
| st.image("https://shap.readthedocs.io/en/latest/_images/shap_header.png", width = 700) | |
| st.caption("By Lundberg, Scott M and Lee, Su-In") | |
| st.caption("Full Citation: https://raw.githubusercontent.com/slundberg/shap/master/docs/references/shap_nips.bib") | |
| st.caption("See on github:: https://github.com/slundberg/shap") | |
| form = st.sidebar.form("Main Settings") | |
| form.header("Main Settings") | |
| task_done = form.selectbox("Which NLP task do you want to solve?", ["Text Generation", "Sentiment Analysis", "Translation", "Summarization"]) | |
| custom_doc = form.checkbox("Use a document from an existing dataset?", value = False) | |
| if custom_doc: | |
| dataset_name = form.text_area("Enter the name of the huggingface Dataset to do analysis of:", value = "Hellisotherpeople/DebateSum") | |
| dataset_name_2 = form.text_area("Enter the name of the config for the dataset if it has one", value = "") | |
| split_name = form.text_area("Enter the name of the split of the dataset that you want to use", value = "train") | |
| number_of_records = form.number_input("Enter the number of documents that you want to analyze from the dataset", value = 200) | |
| column_name = form.text_area("Enter the name of the column that we are doing analysis on (the X value)", value = "Full-Document") | |
| index_to_analyze_start = form.number_input("Enter the index start of the document that you want to analyze of the dataset", value = 1) | |
| index_to_analyze_end = form.number_input("Enter the index end of the document that you want to analyze of the dataset", value = 2) | |
| form.caption("Multiple documents may not work on certain tasks") | |
| else: | |
| doc = st.text_area("Enter a custom document", value = "This is an example custom document") | |
| if task_done == "Text Generation": | |
| model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Text Generation", value = "gpt2") | |
| form.caption("This will download a new model, so it may take awhile or even break if the model is too large") | |
| decoder = form.checkbox("Is this a decoder model?", value = True) | |
| form.caption("This should be true for models like GPT-2, and false for models like BERT") | |
| max_length = form.number_input("What's the max length of the text?", value = 50) | |
| min_length = form.number_input("What's the min length of the text?", value = 20, max_value = max_length) | |
| penalize_repetion = form.number_input("How strongly do we want to penalize repetition in the text generation?", value = 2) | |
| sample = form.checkbox("Shall we use top-k and top-p decoding?", value = True) | |
| form.caption("Setting this to false makes it greedy") | |
| if sample: | |
| top_k = form.number_input("What value of K should we use for Top-K sampling? Set to zero to disable", value = 50) | |
| form.caption("In Top-K sampling, the K most likely next words are filtered and the probability mass is redistributed among only those K next words. ") | |
| top_p = form.number_input("What value of P should we use for Top-p sampling? Set to zero to disable", value = 0.95, max_value = 1.0, min_value = 0.0) | |
| form.caption("Top-p sampling chooses from the smallest possible set of words whose cumulative probability exceeds the probability p. The probability mass is then redistributed among this set of words.") | |
| temperature = form.number_input("How spicy/interesting do we want our models output to be", value = 1.05, min_value = 0.0) | |
| form.caption("Setting this higher decreases the likelihood of high probability words and increases the likelihood of low probability (and presumably more interesting) words") | |
| form.caption("For more details on what these settings mean, see here: https://huggingface.co/blog/how-to-generate") | |
| elif task_done == "Sentiment Analysis": | |
| model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Sentiment Analysis", value = "nateraw/bert-base-uncased-emotion") | |
| rescale_logits = form.checkbox("Do we rescale the probabilities in terms of log odds?", value = False) | |
| elif task_done == "Translation": | |
| model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Translation", value = "Helsinki-NLP/opus-mt-en-es") | |
| elif task_done == "Summarization": | |
| model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Translation", value = "sshleifer/distilbart-xsum-12-6") | |
| else: | |
| model_name = form.text_area("Enter the name of the pre-trained model from transformers that we are using for Question Answering", value = "deepset/roberta-base-squad2") | |
| form.header("Model Explanation Display Settings") | |
| output_width = form.number_input("Enter the number of pixels for width of model explanation html display", value = 800) | |
| output_height = form.number_input("Enter the number of pixels for height of model explanation html display", value = 2000) | |
| form.form_submit_button("Submit") | |
| def load_and_process_data(path, name, streaming, split_name, number_of_records): | |
| dataset = load_dataset(path = path, name = name, streaming=streaming) | |
| #return list(dataset) | |
| dataset_head = dataset[split_name].take(number_of_records) | |
| df = pd.DataFrame.from_dict(dataset_head) | |
| return df[column_name] | |
| def load_model(model_name): | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True) | |
| if task_done == "Text Generation": | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| model.config.is_decoder=decoder | |
| if sample == True: | |
| model.config.task_specific_params["text-generation"] = {"do_sample": sample, "max_length": max_length, "min_length": min_length, "temperature": temperature, "top_k": top_k, "top_p" : top_p, "no_repeat_ngram_size": penalize_repetion} | |
| else: | |
| model.config.task_specific_params["text-generation"] = {"do_sample": sample, "max_length": max_length, "min_length": min_length, "no_repeat_ngram_size": penalize_repetion} | |
| elif task_done == "Sentiment Analysis": | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| elif task_done == "Translation": | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| elif task_done == "Summarization": | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| elif task_done == "Question Answering": | |
| #TODO: This one is going to be harder... | |
| # https://shap.readthedocs.io/en/latest/example_notebooks/text_examples/question_answering/Explaining%20a%20Question%20Answering%20Transformers%20Model.html | |
| model = AutoModelForQuestionAnswering.from_pretrained(model_name) | |
| return tokenizer, model | |
| tokenizer, model = load_model(model_name) | |
| if custom_doc: | |
| df = load_and_process_data(dataset_name, dataset_name_2, True, split_name, number_of_records) | |
| doc = list(df[index_to_analyze_start:index_to_analyze_end]) | |
| st.write(doc) | |
| if task_done == "Sentiment Analysis": | |
| pred = pipeline("text-classification", model=model, tokenizer=tokenizer, return_all_scores=True) | |
| explainer = shap.Explainer(pred, rescale_to_logits = rescale_logits) | |
| else: | |
| explainer = shap.Explainer(model, tokenizer) | |
| if custom_doc: | |
| shap_values = explainer(doc) | |
| else: | |
| shap_values = explainer([doc]) | |
| ###REMEMBER YOU FIXED THE CODE IN SHAP, YOU NEED TO FORK IT | |
| the_plot = shap.plots.text(shap_values, display = False) | |
| components.html(the_plot, height = output_width, width = output_height) | |
| st.caption("Scroll to see the full output!") | |