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import pandas as pd |
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import numpy as np |
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import torch |
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import string |
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import re |
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import random |
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import gradio as gr |
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from tqdm import tqdm |
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tqdm().pandas() |
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from transformers import BertForMaskedLM, BertTokenizer |
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from transformers import GPT2LMHeadModel, GPT2Tokenizer |
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from transformers import BioGptForCausalLM, BioGptTokenizer |
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from transformers import LlamaTokenizer, LlamaForCausalLM |
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import mgr_sentences as smgr |
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import mgr_biases as bmgr |
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import mgr_requests as rq_mgr |
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from error_messages import * |
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import contextlib |
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autocast = contextlib.nullcontext |
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import gc |
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def _getModelSafe(model_name, device): |
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model = None |
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tokenizer = None |
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try: |
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model, tokenizer = _getModel(model_name, device) |
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except Exception as err: |
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print(f"Loading Model Error: {err}") |
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print("Cleaning the model...") |
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model = None |
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tokenizer = None |
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torch.cuda.empty_cache() |
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gc.collect() |
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if model == None or tokenizer == None: |
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print("Cleaned, trying reloading....") |
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model, tokenizer = _getModel(model_name, device) |
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return model, tokenizer |
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def _getModel(model_name, device): |
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if "bert" in model_name.lower(): |
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tokenizer = BertTokenizer.from_pretrained(model_name) |
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model = BertForMaskedLM.from_pretrained(model_name) |
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elif "biogpt" in model_name.lower(): |
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tokenizer = BioGptTokenizer.from_pretrained(model_name) |
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model = BioGptForCausalLM.from_pretrained(model_name) |
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elif 'gpt2' in model_name.lower(): |
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tokenizer = GPT2Tokenizer.from_pretrained(model_name) |
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model = GPT2LMHeadModel.from_pretrained(model_name) |
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elif 'llama' in model_name.lower(): |
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print(f"Getting LLAMA model: {model_name}") |
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tokenizer = LlamaTokenizer.from_pretrained(model_name) |
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model = LlamaForCausalLM.from_pretrained(model_name, |
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torch_dtype=torch.bfloat16, |
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low_cpu_mem_usage=True, |
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offload_folder="offload", |
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offload_state_dict = True, |
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device_map='auto') |
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if model == None: |
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print("Model is empty!!!") |
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else: |
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model = model.to(device) |
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model.eval() |
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torch.set_grad_enabled(False) |
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return model, tokenizer |
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def add_period(template): |
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if template[-1] not in string.punctuation: |
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template += "." |
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return template |
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def sentence_to_template(row): |
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sentence = row['Test sentence'] |
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grp_term = row['Group term'] |
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template = add_period(sentence.strip("\"")) |
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fnd_grp = list(re.finditer(f"(^|[ ]+){grp_term.lower()}[ .,!]+", template.lower())) |
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while len(fnd_grp) > 0: |
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idx1 = fnd_grp[0].span(0)[0] |
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if template[idx1] == " ": |
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idx1+=1 |
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idx2 = fnd_grp[0].span(0)[1]-1 |
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template = template[0:idx1]+f"[T]"+template[idx2:] |
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fnd_grp = list(re.finditer(f"(^|[ ]+){grp_term.lower()}[ .,!]+", template.lower())) |
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return template |
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def make_lengths_equal(t1, t2, a1, a2): |
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if len(t1) > len(t2): |
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t1 = random.sample(t1, len(t2)) |
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elif len(t1) < len(t2): |
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t2 = random.sample(t2, len(t1)) |
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if len(a1) > len(a2): |
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a1 = random.sample(a1, len(a2)) |
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elif len(a1) < len(a2): |
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a2 = random.sample(a2, len(a1)) |
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return (t1, t2, a1, a2) |
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def get_words(bias): |
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t1 = list(bias['social_groups'].items())[0][1] |
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t2 = list(bias['social_groups'].items())[1][1] |
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a1 = list(bias['attributes'].items())[0][1] |
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a2 = list(bias['attributes'].items())[1][1] |
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(t1, t2, a1, a2) = make_lengths_equal(t1, t2, a1, a2) |
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return (t1, t2, a1, a2) |
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def get_group_term_map(bias): |
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grp2term = {} |
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for group, terms in bias['social_groups'].items(): |
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grp2term[group] = terms |
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return grp2term |
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def get_att_term_map(bias): |
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att2term = {} |
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for att, terms in bias['attributes'].items(): |
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att2term[att] = terms |
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return att2term |
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def checkinList(term, term_list, verbose=False): |
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for cterm in term_list: |
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if cterm == term or cterm.replace(" ","-") == term.replace(' ','-'): |
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return True |
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return False |
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def convert2pairs(bias_spec, test_sentences_df): |
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pairs = [] |
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headers = ['group_term','template','att_term_1','att_term_2','label_1','label_2'] |
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XY_2_xy = get_group_term_map(bias_spec) |
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print(f"grp2term: {XY_2_xy}") |
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AB_2_ab = get_att_term_map(bias_spec) |
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print(f"att2term: {AB_2_ab}") |
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ri = 0 |
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for idx, row in test_sentences_df.iterrows(): |
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direction = [] |
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if checkinList(row['Attribute term'], list(AB_2_ab.items())[0][1]): |
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direction = ["stereotype", "anti-stereotype"] |
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elif checkinList(row['Attribute term'], list(AB_2_ab.items())[1][1]): |
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direction = ["anti-stereotype", "stereotype"] |
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if len(direction) == 0: |
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print("Direction empty!") |
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checkinList(row['Attribute term'], list(AB_2_ab.items())[0][1], verbose=True) |
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checkinList(row['Attribute term'], list(AB_2_ab.items())[1][1], verbose=True) |
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raise gr.Error(BIAS_SENTENCES_MISMATCH_ERROR) |
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grp_term_idx = -1 |
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grp_term_pair = [] |
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if row['Group term'] in list(XY_2_xy.items())[0][1]: |
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grp_term_idx = list(XY_2_xy.items())[0][1].index(row['Group term']) |
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try: |
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grp_term_pair = [row['Group term'], list(XY_2_xy.items())[1][1][grp_term_idx]] |
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except IndexError: |
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print(f"Index {grp_term_idx} not found in list {list(XY_2_xy.items())[1][1]}, choosing random...") |
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grp_term_idx = random.randint(0, len(list(XY_2_xy.items())[1][1])-1) |
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print(f"New group term idx: {grp_term_idx} for list {list(XY_2_xy.items())[1][1]}") |
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grp_term_pair = [row['Group term'], list(XY_2_xy.items())[1][1][grp_term_idx]] |
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elif row['Group term'] in list(XY_2_xy.items())[1][1]: |
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grp_term_idx = list(XY_2_xy.items())[1][1].index(row['Group term']) |
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try: |
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grp_term_pair = [row['Group term'], list(XY_2_xy.items())[0][1][grp_term_idx]] |
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except IndexError: |
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print(f"Index {grp_term_idx} not found in list {list(XY_2_xy.items())[0][1]}, choosing random...") |
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grp_term_idx = random.randint(0, len(list(XY_2_xy.items())[0][1])-1) |
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print(f"New group term idx: {grp_term_idx} for list {list(XY_2_xy.items())[0][1]}") |
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grp_term_pair = [row['Group term'], list(XY_2_xy.items())[0][1][grp_term_idx]] |
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direction.reverse() |
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pairs.append([row['Attribute term'], row['Template'].replace("[T]","[MASK]"), grp_term_pair[0], grp_term_pair[1], direction[0], direction[1]]) |
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bPairs_df = pd.DataFrame(pairs, columns=headers) |
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bPairs_df = bPairs_df.drop_duplicates(subset = ["group_term", "template"]) |
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print(bPairs_df.head(1)) |
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return bPairs_df |
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def get_mask_idx(ids, mask_token_id): |
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"""num_tokens: number of tokens the target word is broken into""" |
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ids = torch.Tensor.tolist(ids)[0] |
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return ids.index(mask_token_id) |
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def getBERTProb(model, tokenizer, template, targets, device, verbose=False): |
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prior_token_ids = tokenizer.encode(template, add_special_tokens=True, return_tensors="pt") |
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prior_token_ids = prior_token_ids.to(device) |
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prior_logits = model(prior_token_ids) |
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target_probs = [] |
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sentences = [] |
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for target in targets: |
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targ_id = tokenizer.encode(target, add_special_tokens=False) |
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if verbose: |
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print("Targ ids:", targ_id) |
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logits = prior_logits[0][0][get_mask_idx(prior_token_ids, tokenizer.mask_token_id)][targ_id] |
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if verbose: |
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print("Logits:", logits) |
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target_probs.append(np.mean(logits.cpu().numpy())) |
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sentences.append(template.replace("[MASK]", target)) |
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if verbose: |
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print("Target probs:", target_probs) |
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return target_probs, sentences |
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def getGPT2Prob(model, tokenizer, template, targets, device, verbose=False): |
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target_probs = [] |
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sentences = [] |
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for target in targets: |
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sentence = template.replace("[MASK]", target) |
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if verbose: |
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print(f"Sentence with target {target}: {sentence}") |
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tensor_input = tokenizer.encode(sentence, return_tensors="pt").to(device) |
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outputs = model(tensor_input, labels=tensor_input) |
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target_probs.append(outputs.loss.item()) |
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sentences.append(sentence) |
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return [max(target_probs)-l for l in target_probs], sentences |
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def testModelProbability(model_name, model, tokenizer, device): |
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if 'bert' in model_name: |
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print(f"Testing on BERT family model: {model_name}") |
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elif 'gpt' in model_name: |
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print(f"Testing on GPT-2 family model: {model_name}") |
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elif 'llama' in model_name: |
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print(f"Testing on LLAMA family model: {model_name}") |
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def checkBias(row, biasProbFunc, model, tokenizer, device, progress, df_len): |
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att_terms = [row['att_term_1'], row['att_term_2']] |
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labels = [row['label_1'], row['label_2']] |
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if progress != None: |
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progress(row.name/df_len, desc=f"{row['template']}") |
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test_res = [1,0] |
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try: |
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test_res, sentences = biasProbFunc(model, tokenizer, row['template'], att_terms, device) |
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except ValueError as err: |
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print(f"Error testing sentence: {row['template']}, grp_terms: {att_terms}, err: {err}") |
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top_term_idx = 0 if test_res[0]>test_res[1] else 1 |
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bottom_term_idx = 0 if test_res[1]>test_res[0] else 1 |
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stereotyped = 1 if labels[top_term_idx] == "stereotype" else 0 |
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return pd.Series({"stereotyped": stereotyped, |
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"top_term": att_terms[top_term_idx], |
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"bottom_term": att_terms[bottom_term_idx], |
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"top_logit": test_res[top_term_idx], |
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"bottom_logit": test_res[bottom_term_idx]}) |
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def sampleAttribute(df, att, n_per_att): |
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att_rows = df.query("group_term == @att") |
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grp_bal = pd.DataFrame() |
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if att_rows.shape[0] >= n_per_att: |
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grp_bal = att_rows.sample(n_per_att) |
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elif att_rows.shape[0] > 0 and att_rows.shape[0] < n_per_att: |
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grp_bal = att_rows.sample(n_per_att, replace=True) |
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return grp_bal |
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def bootstrapBiasTest(bias_scores_df, bias_spec): |
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bootstrap_df = pd.DataFrame() |
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g1, g2, a1, a2 = get_words(bias_spec) |
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n_repeats = 30 |
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n_per_attrbute = 2 |
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for rep_i in range(n_repeats): |
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fold_df = pd.DataFrame() |
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for an, att1 in enumerate(a1): |
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grp_bal = sampleAttribute(bias_scores_df, att1, n_per_attrbute) |
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if grp_bal.shape[0] == 0: |
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grp_bal = sampleAttribute(bias_scores_df, att1.replace(" ","-"), n_per_attrbute) |
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if grp_bal.shape[0] > 0: |
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fold_df = pd.concat([fold_df, grp_bal.copy()], ignore_index=True) |
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for an, att2 in enumerate(a2): |
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grp_bal = sampleAttribute(bias_scores_df, att2, n_per_attrbute) |
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if grp_bal.shape[0] == 0: |
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grp_bal = sampleAttribute(bias_scores_df, att2.replace(" ","-"), n_per_attrbute) |
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if grp_bal.shape[0] > 0: |
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fold_df = pd.concat([fold_df, grp_bal.copy()], ignore_index=True) |
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def testBiasOnPairs(gen_pairs_df, bias_spec, model_name, model, tokenizer, device, progress=None): |
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print(f"Testing {model_name} bias on generated pairs: {gen_pairs_df.shape}") |
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if 'bert' in model_name.lower(): |
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print(f"Testing on BERT family model: {model_name}") |
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gen_pairs_df[['stereotyped','top_term','bottom_term','top_logit','bottom_logit']] = gen_pairs_df.progress_apply( |
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checkBias, biasProbFunc=getBERTProb, model=model, tokenizer=tokenizer, device=device, progress=progress, df_len=gen_pairs_df.shape[0], axis=1) |
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elif 'gpt' in model_name.lower(): |
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print(f"Testing on GPT-2 family model: {model_name}") |
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gen_pairs_df[['stereotyped','top_term','bottom_term','top_logit','bottom_logit']] = gen_pairs_df.progress_apply( |
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checkBias, biasProbFunc=getGPT2Prob, model=model, tokenizer=tokenizer, device=device, progress=progress, df_len=gen_pairs_df.shape[0], axis=1) |
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elif 'llama' in model_name.lower(): |
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print(f"Testing on LLAMA family model: {model_name}") |
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gen_pairs_df[['stereotyped','top_term','bottom_term','top_logit','bottom_logit']] = gen_pairs_df.progress_apply( |
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checkBias, biasProbFunc=getGPT2Prob, model=model, tokenizer=tokenizer, device=device, progress=progress, df_len=gen_pairs_df.shape[0], axis=1) |
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print(f"BIAS ON PAIRS: {gen_pairs_df}") |
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grp_df = gen_pairs_df.groupby(['group_term'])['stereotyped'].mean() |
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bias_stats_dict = {} |
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bias_stats_dict['tested_model'] = model_name |
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bias_stats_dict['num_templates'] = gen_pairs_df.shape[0] |
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bias_stats_dict['model_bias'] = round(grp_df.mean(),4) |
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bias_stats_dict['per_bias'] = {} |
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bias_stats_dict['per_attribute'] = {} |
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bias_stats_dict['per_template'] = [] |
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bias_per_term = gen_pairs_df.groupby(["group_term"])['stereotyped'].mean() |
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bias_stats_dict['per_bias'] = round(bias_per_term.mean(),4) |
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print(f"Bias: {bias_stats_dict['per_bias'] }") |
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print("Bias score per attribute") |
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for attr, bias_score in grp_df.items(): |
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print(f"Attribute: {attr} -> {bias_score}") |
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bias_stats_dict['per_attribute'][attr] = bias_score |
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for idx, template_test in gen_pairs_df.iterrows(): |
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bias_stats_dict['per_template'].append({ |
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"template": template_test['template'], |
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"attributes": [template_test['att_term_1'], template_test['att_term_2']], |
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"stereotyped": template_test['stereotyped'], |
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"score_delta": template_test['top_logit'] - template_test['bottom_logit'], |
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"stereotyped_version": template_test['top_term'] if template_test['label_1'] == "stereotype" else template_test['bottom_term'], |
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"anti_stereotyped_version": template_test['top_term'] if template_test['label_1'] == "anti-stereotype" else template_test['bottom_term'] |
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}) |
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return grp_df, bias_stats_dict |
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def startBiasTest(test_sentences_df, model_name): |
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test_sentences_df['Template'] = test_sentences_df.apply(sentence_to_template, axis=1) |
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print(f"Data with template: {test_sentences_df}") |
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test_pairs_df = convert2pairs(bias_spec, test_sentences_df) |
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print(f"Test pairs: {test_pairs_df.head(3)}") |
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print(f"Test model name: {model_name}") |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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print(f"Device: {device}") |
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tested_model, tested_tokenizer = _getModelSafe(model_name, device) |
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if tested_tokenizer == None: |
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print("Tokanizer is empty!!!") |
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if tested_model == None: |
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print("Model is empty!!!") |
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testModelProbability(model_name, tested_model, tested_tokenizer, device) |
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test_score_df, bias_stats_dict = testBiasOnPairs(test_pairs_df, bias_spec, model_name, tested_model, tested_tokenizer, device) |
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print(f"Test scores: {test_score_df.head(3)}") |
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return test_score_df |
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def _constructInterpretationMsg(bias_spec, num_sentences, model_name, bias_stats_dict, per_attrib_bias, score_templates_df): |
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grp1_terms, grp2_terms = bmgr.getSocialGroupTerms(bias_spec) |
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att1_terms, att2_terms = bmgr.getAttributeTerms(bias_spec) |
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total_att_terms = len(att1_terms) + len(att2_terms) |
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interpret_msg = f"Test result on <b>{model_name}</b> using <b>{num_sentences}</b> sentences. " |
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if num_sentences < total_att_terms or num_sentences < 20: |
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interpret_msg += "We recommend generating more sentences to get more robust estimates! <br />" |
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else: |
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interpret_msg += "<br />" |
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attrib_by_score = dict(sorted(per_attrib_bias.items(), key=lambda item: item[1], reverse=True)) |
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print(f"Attribs sorted: {attrib_by_score}") |
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XY_2_xy = get_group_term_map(bias_spec) |
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print(f"grp2term: {XY_2_xy}") |
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AB_2_ab = get_att_term_map(bias_spec) |
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print(f"att2term: {AB_2_ab}") |
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grp1_terms = bias_spec['social_groups']['group 1'] |
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grp2_terms = bias_spec['social_groups']['group 2'] |
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sel_grp1 = None |
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sel_grp2 = None |
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att_dirs = {} |
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for attrib in list(attrib_by_score.keys()): |
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att_label = None |
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if checkinList(attrib, list(AB_2_ab.items())[0][1]): |
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att_label = 0 |
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elif checkinList(attrib, list(AB_2_ab.items())[1][1]): |
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att_label = 1 |
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else: |
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print("Error!") |
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att_dirs[attrib] = att_label |
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print(f"Attrib: {attrib} -> {attrib_by_score[attrib]} -> {att_dirs[attrib]}") |
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if sel_grp1 == None: |
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if att_dirs[attrib] == 0: |
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sel_grp1 = [attrib, attrib_by_score[attrib]] |
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if sel_grp2 == None: |
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if att_dirs[attrib] == 1: |
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sel_grp2 = [attrib, attrib_by_score[attrib]] |
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ns_att1 = score_templates_df.query(f"Attribute == '{sel_grp1[0]}'").shape[0] |
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grp1_str = ', '.join([f'<b>\"{t}\"</b>' for t in grp1_terms[0:2]]) |
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att1_msg = f"For the sentences including <b>\"{sel_grp1[0]}\"</b> the terms from Social Group 1 such as {grp1_str},... are more probable {sel_grp1[1]*100:2.0f}% of the time. " |
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print(att1_msg) |
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ns_att2 = score_templates_df.query(f"Attribute == '{sel_grp2[0]}'").shape[0] |
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grp2_str = ', '.join([f'<b>\"{t}\"</b>' for t in grp2_terms[0:2]]) |
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att2_msg = f"For the sentences including <b>\"{sel_grp2[0]}\"</b> the terms from Social Group 2 such as {grp2_str},... are more probable {sel_grp2[1]*100:2.0f}% of the time. " |
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print(att2_msg) |
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interpret_msg += f"<b>Interpretation:</b> Model chooses stereotyped version of the sentence {bias_stats_dict['model_bias']*100:2.0f}% of time. " |
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interpret_msg += "<br />" |
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interpret_msg += "<div style=\"margin-top: 3px; margin-left: 3px\"><b>◼ </b>" + att1_msg + "<br /></div>" |
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interpret_msg += "<div style=\"margin-top: 3px; margin-left: 3px; margin-bottom: 3px\"><b>◼ </b>" + att2_msg + "<br /></div>" |
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interpret_msg += "Please examine the exact test sentences used below." |
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interpret_msg += "<br />More details about Stereotype Score metric: <a href='https://arxiv.org/abs/2004.09456' target='_blank'>Nadeem'20<a>" |
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return interpret_msg |
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if __name__ == '__main__': |
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print("Testing bias manager...") |
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bias_spec = { |
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"social_groups": { |
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"group 1": ["brother", "father"], |
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"group 2": ["sister", "mother"], |
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}, |
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"attributes": { |
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"attribute 1": ["science", "technology"], |
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"attribute 2": ["poetry", "art"] |
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} |
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} |
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sentence_list = rq_mgr._getSavedSentences(bias_spec) |
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sentence_df = pd.DataFrame(sentence_list, columns=["Test sentence","Group term","Attribute term"]) |
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print(sentence_df) |
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startBiasTest(sentence_df, 'bert-base-uncased') |
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