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import gradio as gr |
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import matplotlib.pyplot as plt |
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import numpy as np |
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import pandas as pd |
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import random |
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from matplotlib.ticker import MaxNLocator |
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from transformers import pipeline |
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MODEL_NAMES = ["bert-base-uncased", "roberta-base", "bert-large-uncased", "roberta-large"] |
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OWN_MODEL_NAME = 'add-a-model' |
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DECIMAL_PLACES = 1 |
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EPS = 1e-5 |
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DATE_SPLIT_KEY = "DATE" |
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START_YEAR = 1801 |
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STOP_YEAR = 1999 |
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NUM_PTS = 20 |
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DATES = np.linspace(START_YEAR, STOP_YEAR, NUM_PTS).astype(int).tolist() |
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DATES = [f'{d}' for d in DATES] |
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PLACE_SPLIT_KEY = "PLACE" |
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PLACES = [ |
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"Afghanistan", |
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"Yemen", |
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"Iraq", |
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"Pakistan", |
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"Syria", |
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"Democratic Republic of Congo", |
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"Iran", |
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"Mali", |
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"Chad", |
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"Saudi Arabia", |
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"Switzerland", |
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"Ireland", |
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"Lithuania", |
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"Rwanda", |
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"Namibia", |
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"Sweden", |
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"New Zealand", |
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"Norway", |
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"Finland", |
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"Iceland"] |
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SUBREDDITS = [ |
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"GlobalOffensive", |
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"pcmasterrace", |
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"nfl", |
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"sports", |
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"The_Donald", |
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"leagueoflegends", |
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"Overwatch", |
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"gonewild", |
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"Futurology", |
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"space", |
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"technology", |
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"gaming", |
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"Jokes", |
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"dataisbeautiful", |
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"woahdude", |
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"askscience", |
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"wow", |
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"anime", |
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"BlackPeopleTwitter", |
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"politics", |
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"pokemon", |
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"worldnews", |
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"reddit.com", |
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"interestingasfuck", |
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"videos", |
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"nottheonion", |
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"television", |
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"science", |
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"atheism", |
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"movies", |
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"gifs", |
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"Music", |
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"trees", |
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"EarthPorn", |
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"GetMotivated", |
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"pokemongo", |
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"news", |
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"Fitness", |
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"Showerthoughts", |
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"OldSchoolCool", |
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"explainlikeimfive", |
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"todayilearned", |
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"gameofthrones", |
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"AdviceAnimals", |
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"DIY", |
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"WTF", |
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"IAmA", |
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"cringepics", |
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"tifu", |
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"mildlyinteresting", |
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"funny", |
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"pics", |
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"LifeProTips", |
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"creepy", |
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"personalfinance", |
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"food", |
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"AskReddit", |
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"books", |
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"aww", |
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"sex", |
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"relationships", |
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] |
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GENDERED_LIST = [ |
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['he', 'she'], |
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['him', 'her'], |
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['his', 'hers'], |
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["himself", "herself"], |
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['male', 'female'], |
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['man', 'woman'], |
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['men', 'women'], |
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["husband", "wife"], |
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['father', 'mother'], |
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['boyfriend', 'girlfriend'], |
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['brother', 'sister'], |
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["actor", "actress"], |
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] |
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models = dict() |
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for bert_like in MODEL_NAMES: |
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models[bert_like] = pipeline("fill-mask", model=bert_like) |
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def get_gendered_token_ids(): |
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male_gendered_tokens = [list[0] for list in GENDERED_LIST] |
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female_gendered_tokens = [list[1] for list in GENDERED_LIST] |
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return male_gendered_tokens, female_gendered_tokens |
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def prepare_text_for_masking(input_text, mask_token, gendered_tokens, split_key): |
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text_w_masks_list = [ |
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mask_token if word.lower() in gendered_tokens else word for word in input_text.split()] |
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num_masks = len([m for m in text_w_masks_list if m == mask_token]) |
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text_portions = ' '.join(text_w_masks_list).split(split_key) |
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return text_portions, num_masks |
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def get_avg_prob_from_pipeline_outputs(mask_filled_text, gendered_token, num_preds): |
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pronoun_preds = [sum([ |
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pronoun["score"] if pronoun["token_str"].strip().lower() in gendered_token else 0.0 |
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for pronoun in top_preds]) |
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for top_preds in mask_filled_text |
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] |
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return round(sum(pronoun_preds) / (EPS + num_preds) * 100, DECIMAL_PLACES) |
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def get_figure(df, gender, n_fit=1): |
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df = df.set_index('x-axis') |
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cols = df.columns |
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xs = list(range(len(df))) |
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ys = df[cols[0]] |
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fig, ax = plt.subplots() |
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fig.set_figheight(3) |
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fig.set_figwidth(9) |
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p, C_p = np.polyfit(xs, ys, n_fit, cov=1) |
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t = np.linspace(min(xs)-1, max(xs)+1, 10*len(xs)) |
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TT = np.vstack([t**(n_fit-i) for i in range(n_fit+1)]).T |
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yi = np.dot(TT, p) |
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C_yi = np.dot(TT, np.dot(C_p, TT.T)) |
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sig_yi = np.sqrt(np.diag(C_yi)) |
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ax.fill_between(t, yi+sig_yi, yi-sig_yi, alpha=.25) |
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ax.plot(t, yi, '-') |
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ax.plot(df, 'ro') |
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ax.legend(list(df.columns)) |
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ax.axis('tight') |
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ax.set_xlabel("Value injected into input text") |
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ax.set_title( |
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f"Probability of predicting {gender} pronouns.") |
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ax.set_ylabel(f"Softmax prob for pronouns") |
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ax.xaxis.set_major_locator(MaxNLocator(6)) |
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ax.tick_params(axis='x', labelrotation=5) |
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return fig |
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def predict_gender_pronouns( |
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model_name, |
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own_model_name, |
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indie_vars, |
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split_key, |
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normalizing, |
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n_fit, |
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input_text, |
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): |
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"""Run inference on input_text for each model type, returning df and plots of percentage |
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of gender pronouns predicted as female and male in each target text. |
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""" |
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if model_name not in MODEL_NAMES: |
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model = pipeline("fill-mask", model=own_model_name) |
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else: |
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model = models[model_name] |
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mask_token = model.tokenizer.mask_token |
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indie_vars_list = indie_vars.split(',') |
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male_gendered_tokens, female_gendered_tokens = get_gendered_token_ids() |
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text_segments, num_preds = prepare_text_for_masking( |
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input_text, mask_token, male_gendered_tokens + female_gendered_tokens, split_key) |
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male_pronoun_preds = [] |
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female_pronoun_preds = [] |
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for indie_var in indie_vars_list: |
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target_text = f"{indie_var}".join(text_segments) |
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mask_filled_text = model(target_text) |
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if type(mask_filled_text[0]) is not list: |
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mask_filled_text = [mask_filled_text] |
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female_pronoun_preds.append(get_avg_prob_from_pipeline_outputs( |
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mask_filled_text, |
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female_gendered_tokens, |
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num_preds |
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)) |
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male_pronoun_preds.append(get_avg_prob_from_pipeline_outputs( |
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mask_filled_text, |
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male_gendered_tokens, |
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num_preds |
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)) |
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if normalizing: |
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total_gendered_probs = np.add( |
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female_pronoun_preds, male_pronoun_preds) |
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female_pronoun_preds = np.around( |
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np.divide(female_pronoun_preds, total_gendered_probs+EPS)*100, |
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decimals=DECIMAL_PLACES |
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) |
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male_pronoun_preds = np.around( |
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np.divide(male_pronoun_preds, total_gendered_probs+EPS)*100, |
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decimals=DECIMAL_PLACES |
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) |
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results_df = pd.DataFrame({'x-axis': indie_vars_list}) |
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results_df['female_pronouns'] = female_pronoun_preds |
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results_df['male_pronouns'] = male_pronoun_preds |
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female_fig = get_figure(results_df.drop( |
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'male_pronouns', axis=1), 'female', n_fit,) |
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male_fig = get_figure(results_df.drop( |
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'female_pronouns', axis=1), 'male', n_fit,) |
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display_text = f"{random.choice(indie_vars_list)}".join(text_segments) |
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return ( |
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display_text, |
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female_fig, |
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male_fig, |
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results_df, |
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) |
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title = "Causing Gender Pronouns" |
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description = """ |
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## Intro |
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""" |
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date_example = [ |
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MODEL_NAMES[1], |
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'', |
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', '.join(DATES), |
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'DATE', |
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"False", |
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1, |
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'She was a teenager in DATE.' |
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] |
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place_example = [ |
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MODEL_NAMES[0], |
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'', |
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', '.join(PLACES), |
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'PLACE', |
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"False", |
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1, |
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'She became an adult in PLACE.' |
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] |
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subreddit_example = [ |
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MODEL_NAMES[3], |
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'', |
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', '.join(SUBREDDITS), |
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'SUBREDDIT', |
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"False", |
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1, |
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'She was a kid. SUBREDDIT.' |
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] |
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own_model_example = [ |
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OWN_MODEL_NAME, |
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'emilyalsentzer/Bio_ClinicalBERT', |
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', '.join(DATES), |
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'DATE', |
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"False", |
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1, |
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'She was exposed to the virus in DATE.' |
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] |
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def date_fn(): |
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return date_example |
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def place_fn(): |
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return place_example |
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def reddit_fn(): |
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return subreddit_example |
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def your_fn(): |
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return own_model_example |
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demo = gr.Blocks() |
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with demo: |
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gr.Markdown("# Spurious Correlation Evaluation for Pre-trained LLMs") |
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gr.Markdown("Find spurious correlations between seemingly independent variables (for example between `gender` and `time`) in almost any BERT-like LLM on Hugging Face, below.") |
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gr.Markdown("See why this happens in [Exploiting Selection Bias on Underspecified Tasks in Large Language Models](https://arxiv.org/abs/2210.00131).") |
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gr.Markdown("## Instructions for this Demo") |
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gr.Markdown("1) Click on one of the examples below (where we sweep through a spectrum of `places`, `dates` and `subreddits`) to pre-populate the input fields.") |
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gr.Markdown("2) Check out the pre-populated fields as you scroll down to the ['Hit Submit...'] button!") |
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gr.Markdown("3) Repeat steps (1) and (2) with more pre-populated inputs or with your own values in the input fields!") |
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gr.Markdown("## Example inputs") |
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gr.Markdown("Click a button below to pre-populate input fields with example values. Then scroll down to Hit Submit to generate predictions.") |
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with gr.Row(): |
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date_gen = gr.Button('Click for date example inputs') |
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gr.Markdown("<-- x-axis sorted by older to more recent dates:") |
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place_gen = gr.Button('Click for country example inputs') |
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gr.Markdown( |
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"<-- x-axis sorted by bottom 10 and top 10 [Global Gender Gap](https://www3.weforum.org/docs/WEF_GGGR_2021.pdf) ranked countries:") |
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subreddit_gen = gr.Button('Click for Subreddit example inputs') |
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gr.Markdown( |
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"<-- x-axis sorted in order of increasing self-identified female participation (see [bburky](http://bburky.com/subredditgenderratios/)): ") |
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your_gen = gr.Button('Add-a-model example inputs') |
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gr.Markdown("<-- x-axis dates, with your own model loaded! (If first time, try another example, it can take a while to load new model.)") |
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gr.Markdown("## Input fields") |
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gr.Markdown( |
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f"A) Pick a spectrum of comma separated values for text injection and x-axis.") |
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with gr.Row(): |
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x_axis = gr.Textbox( |
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lines=3, |
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label="A) Comma separated values for text injection and x-axis", |
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) |
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gr.Markdown("B) Pick a pre-loaded BERT-family model of interest on the right.") |
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gr.Markdown(f"Or C) select `{OWN_MODEL_NAME}`, then add the mame of any other Hugging Face model that supports the [fill-mask](https://huggingface.co/models?pipeline_tag=fill-mask) task on the right (note: this may take some time to load).") |
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with gr.Row(): |
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model_name = gr.Radio( |
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MODEL_NAMES + [OWN_MODEL_NAME], |
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type="value", |
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label="B) BERT-like model.", |
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) |
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own_model_name = gr.Textbox( |
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label="C) If you selected an 'add-a-model' model, put any Hugging Face pipeline model name (that supports the fill-mask task) here.", |
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) |
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gr.Markdown("D) Pick if you want to the predictions normalied to these gendered terms only.") |
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gr.Markdown("E) Also tell the demo what special token you will use in your input text, that you would like replaced with the spectrum of values you listed above.") |
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gr.Markdown("And F) the degree of polynomial fit used for high-lighting potential spurious association.") |
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with gr.Row(): |
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to_normalize = gr.Dropdown( |
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["False", "True"], |
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label="D) Normalize model's predictions to only the gendered ones?", |
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type="index", |
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) |
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place_holder = gr.Textbox( |
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label="E) Special token place-holder", |
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) |
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n_fit = gr.Dropdown( |
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list(range(1, 5)), |
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label="F) Degree of polynomial fit", |
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type="value", |
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) |
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gr.Markdown( |
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"G) Finally, add input text that includes at least one gendered pronouns and one place-holder token specified above.") |
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with gr.Row(): |
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input_text = gr.Textbox( |
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lines=2, |
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label="G) Input text with pronouns and place-holder token", |
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) |
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gr.Markdown("## Outputs!") |
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with gr.Row(): |
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btn = gr.Button("Hit submit to generate predictions!") |
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with gr.Row(): |
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sample_text = gr.Textbox( |
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type="auto", label="Output text: Sample of text fed to model") |
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with gr.Row(): |
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female_fig = gr.Plot(type="auto") |
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male_fig = gr.Plot(type="auto") |
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with gr.Row(): |
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df = gr.Dataframe( |
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show_label=True, |
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overflow_row_behaviour="show_ends", |
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label="Table of softmax probability for pronouns predictions", |
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) |
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with gr.Row(): |
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date_gen.click(date_fn, inputs=[], outputs=[model_name, own_model_name, |
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x_axis, place_holder, to_normalize, n_fit, input_text]) |
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place_gen.click(place_fn, inputs=[], outputs=[ |
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model_name, own_model_name, x_axis, place_holder, to_normalize, n_fit, input_text]) |
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subreddit_gen.click(reddit_fn, inputs=[], outputs=[ |
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model_name, own_model_name, x_axis, place_holder, to_normalize, n_fit, input_text]) |
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your_gen.click(your_fn, inputs=[], outputs=[ |
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model_name, own_model_name, x_axis, place_holder, to_normalize, n_fit, input_text]) |
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btn.click( |
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predict_gender_pronouns, |
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inputs=[model_name, own_model_name, x_axis, place_holder, |
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to_normalize, n_fit, input_text], |
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outputs=[sample_text, female_fig, male_fig, df]) |
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demo.launch(debug=True) |
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