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import gradio as gr |
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from transformers import pipeline |
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from matplotlib.ticker import MaxNLocator |
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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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MODEL_NAMES = ["bert-base-uncased", |
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"distilbert-base-uncased", "xlm-roberta-base"] |
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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 = 1800 |
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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_paths = dict() |
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models = dict() |
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for bert_like in MODEL_NAMES: |
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models_paths[bert_like] = bert_like |
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models[bert_like] = pipeline( |
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"fill-mask", model=models_paths[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 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"].lower( |
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) 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(4) |
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fig.set_figwidth(8) |
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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=15) |
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return fig |
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def predict_gender_pronouns( |
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model_type, |
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indie_vars, |
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split_key, |
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normalizing, |
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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 precentage |
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of gender pronouns predicted as female and male in each target text. |
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""" |
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model = models[model_type] |
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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') |
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male_fig = get_figure(results_df.drop( |
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'female_pronouns', axis=1), 'male') |
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return ( |
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target_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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place_example = [ |
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MODEL_NAMES[0], |
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', '.join(PLACES), |
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'PLACE', |
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"False", |
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'Born in PLACE, she was a teacher.' |
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] |
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date_example = [ |
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MODEL_NAMES[0], |
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', '.join(DATES), |
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'DATE', |
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"False", |
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'Born in DATE, she was a doctor.' |
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] |
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subreddit_example = [ |
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MODEL_NAMES[2], |
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', '.join(SUBREDDITS), |
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'SUBREDDIT', |
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"False", |
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'I saw on r/SUBREDDIT that she is a hacker.' |
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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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demo = gr.Blocks() |
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with demo: |
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gr.Markdown("## Hunt for spurious correlations in our LLMs.") |
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gr.Markdown("Although genders are relatively evenly distributed across time, place and interests, there are also known gender disparities in terms of access to resources. We suggest that this access disparity can result in dataset selection bias, causing models to learn a surprising range of spurious associations.") |
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gr.Markdown("These spurious associations are often considered undesirable, as they do not match our intuition about the real-world domain from which we derive samples for inference-time prediction.") |
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gr.Markdown("Selection bias of samples into datasets is a zero-sum-game, with even our high quality datasets forced to trade off one for another, thus inducing selection bias into the learned associations of the model.") |
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gr.Markdown("One intuitive way to see the impact that changing one variable may have upon another is to look for a dose-response relationship, in which a larger intervention in the treatment (the value in text form injected in the otherwise unchanged text sample) produces a larger response in the output (the softmax probability of a gendered pronoun). Specifically, below are examples of sweeping through a spectrum of place, date and subreddit interest (we encourage you to try your own).") |
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gr.Markdown("This requires a spectrum of less to more gender-equal values for each covariate. For date, it’s easy to just use time itself, as gender equality has generally improved with time, so we picked years ranging from 1800 - 1999. For place we used the bottom and top 10 Global Gender Gap ranked countries. And for subreddit, we use subreddit name ordered by subreddits that have an increasingly larger percentage of self-reported female commenters.") |
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with gr.Row(): |
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x_axis = gr.Textbox( |
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lines=5, |
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label="Pick a spectrum of values for text injection and x-axis", |
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) |
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with gr.Row(): |
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model_name = gr.Radio( |
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MODEL_NAMES, |
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type="value", |
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label="Pick a BERT-like model.", |
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) |
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place_holder = gr.Textbox( |
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label="Special token used in input text that will be replaced with the above spectrum of values.", |
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type="index", |
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) |
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to_normalize = gr.Dropdown( |
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["False", "True"], |
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label="Normalize?", |
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type="index", |
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) |
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with gr.Row(): |
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input_text = gr.Textbox( |
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lines=5, |
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label="Input Text: Sentence about a single person using some gendered pronouns to refer to them.", |
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) |
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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( |
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type="auto", label="Plot of softmax probability pronouns predicted female.") |
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male_fig = gr.Plot( |
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type="auto", label="Plot of softmax probability pronouns predicted male.") |
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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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gr.Markdown("X-axis sorted by older to more recent dates:") |
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place_gen = gr.Button('Populate fields with a location example') |
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gr.Markdown("X-axis sorted by bottom 10 and top 10 Global Gender Gap ranked countries:") |
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date_gen = gr.Button('Populate fields with a date example') |
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gr.Markdown("X-axis sorted in order of increasing self-identified female participation (see [bburky demo](http://bburky.com/subredditgenderratios/)): ") |
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subreddit_gen = gr.Button('Populate fields with a subreddit example') |
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with gr.Row(): |
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date_gen.click(date_fn, inputs=[], outputs=[model_name, |
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x_axis, place_holder, to_normalize, input_text]) |
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place_gen.click(place_fn, inputs=[], outputs=[ |
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model_name, x_axis, place_holder, to_normalize, input_text]) |
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subreddit_gen.click(reddit_fn, inputs=[], outputs=[ |
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model_name, x_axis, place_holder, to_normalize, input_text]) |
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with gr.Row(): |
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btn = gr.Button("Hit submit") |
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btn.click( |
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predict_gender_pronouns, |
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inputs=[model_name, x_axis, place_holder, |
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to_normalize, 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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