from typing import Optional
import gradio as gr
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
from transformers import pipeline
import pandas as pd
import numpy as np
# Play with me, consts
CONDITIONING_VARIABLES = ["none", "birth_place", "birth_date", "name"]
FEMALE_WEIGHTS = [1.5, 5] # About 5x more male than female tokens in dataset
BERT_LIKE_MODELS = ["bert", "distilbert"]
# Internal consts
START_YEAR = 1800
STOP_YEAR = 1999
SPLIT_KEY = "DATE"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MAX_TOKEN_LENGTH = 128
NON_LOSS_TOKEN_ID = -100
NON_GENDERED_TOKEN_ID = 30 # Picked an int that will pop out visually
# Picked an int that will pop out visually
LABEL_DICT = {"female": 9, "male": -9}
CLASSES = list(LABEL_DICT.keys())
# Fire up the models
models_paths = dict()
models = dict()
base_path = "emilylearning/"
for var in CONDITIONING_VARIABLES:
for f_weight in FEMALE_WEIGHTS:
if f_weight == 1.5:
models_paths[(var, f_weight)] = (
base_path
+ f"finetuned_cgp_added_{var}__female_weight_{f_weight}__test_run_False__p_dataset_100"
)
else:
models_paths[(var, f_weight)] = (
base_path
+ f"finetuned_cgp_add_{var}__f_weight_{f_weight}__p_dataset_100__test_False"
)
models[(var, f_weight)] = AutoModelForTokenClassification.from_pretrained(
models_paths[(var, f_weight)]
)
for bert_like in BERT_LIKE_MODELS:
models_paths[(bert_like,)] = f"{bert_like}-base-uncased"
models[(bert_like,)] = pipeline(
"fill-mask", model=models_paths[(bert_like,)])
# Tokenizers same for each model, so just grabbing one of them
tokenizer = AutoTokenizer.from_pretrained(
models_paths[(CONDITIONING_VARIABLES[0], FEMALE_WEIGHTS[0])
], add_prefix_space=True
)
MASK_TOKEN_ID = tokenizer.mask_token_id
# more static stuff
gendered_lists = [
["he", "she"],
["him", "her"],
["his", "hers"],
["male", "female"],
["man", "woman"],
["men", "women"],
["husband", "wife"],
]
male_gendered_tokens = [list[0] for list in gendered_lists]
female_gendered_tokens = [list[1] for list in gendered_lists]
male_gendered_token_ids = tokenizer.convert_tokens_to_ids(male_gendered_tokens)
female_gendered_token_ids = tokenizer.convert_tokens_to_ids(
female_gendered_tokens)
assert tokenizer.unk_token_id not in male_gendered_token_ids
assert tokenizer.unk_token_id not in female_gendered_token_ids
label_list = list(LABEL_DICT.values())
assert label_list[0] == LABEL_DICT["female"], "LABEL_DICT not an ordered dict"
label2id = {label: idx for idx, label in enumerate(label_list)}
def tokenize_and_append_metadata(text, tokenizer):
tokenized = tokenizer(
text,
truncation=True,
padding=True,
max_length=MAX_TOKEN_LENGTH,
)
"""Tokenize text and mask/flag 'gendered_tokens_ids' in token_ids and labels."""
# Finding the gender pronouns in the tokens
token_ids = tokenized["input_ids"]
female_tags = torch.tensor(
[
LABEL_DICT["female"]
if id in female_gendered_token_ids
else NON_GENDERED_TOKEN_ID
for id in token_ids
]
)
male_tags = torch.tensor(
[
LABEL_DICT["male"]
if id in male_gendered_token_ids
else NON_GENDERED_TOKEN_ID
for id in token_ids
]
)
# Labeling and masking out occurrences of gendered pronouns
labels = torch.tensor([NON_LOSS_TOKEN_ID] * len(token_ids))
labels = torch.where(
female_tags == LABEL_DICT["female"],
label2id[LABEL_DICT["female"]],
NON_LOSS_TOKEN_ID,
)
labels = torch.where(
male_tags == LABEL_DICT["male"], label2id[LABEL_DICT["male"]], labels
)
masked_token_ids = torch.where(
female_tags == LABEL_DICT["female"], MASK_TOKEN_ID, torch.tensor(
token_ids)
)
masked_token_ids = torch.where(
male_tags == LABEL_DICT["male"], MASK_TOKEN_ID, masked_token_ids
)
tokenized["input_ids"] = masked_token_ids
tokenized["labels"] = labels
return tokenized
def get_tokenized_text_with_years(years, input_text):
"""Construct dict of tokenized texts with each year injected into the text."""
text_portions = input_text.split(SPLIT_KEY)
tokenized_w_year = {'ids': [], 'atten_mask': [], 'toks': [], 'labels': []}
for b_date in years:
target_text = f"{b_date}".join(text_portions)
tokenized_sample = tokenize_and_append_metadata(
target_text,
tokenizer=tokenizer,
)
tokenized_w_year['ids'].append(tokenized_sample["input_ids"])
tokenized_w_year['atten_mask'].append(
torch.tensor(tokenized_sample["attention_mask"]))
tokenized_w_year['toks'].append(
tokenizer.convert_ids_to_tokens(tokenized_sample["input_ids"]))
tokenized_w_year['labels'].append(tokenized_sample["labels"])
return tokenized_w_year
def predict_gender_pronouns(
num_points, conditioning_variables, f_weights, bert_like_models, input_text
):
"""Run inference on input_text for each model type, returning df and plots of precentage
of gender pronouns predicted as female and male in each target text.
"""
years = np.linspace(START_YEAR, STOP_YEAR, int(num_points)).astype(int)
tokenized = get_tokenized_text_with_years(years, input_text)
is_masked = tokenized['ids'][0] == MASK_TOKEN_ID
num_preds = torch.sum(is_masked).item()
dfs = []
dfs.append(pd.DataFrame({"year": years}))
for f_weight in f_weights:
for var in conditioning_variables:
prefix = f"{var}_w{f_weight}"
model = models[(var, f_weight)]
p_female = []
p_male = []
for year_idx in range(len(tokenized['ids'])):
ids = tokenized["ids"][year_idx]
atten_mask = tokenized["atten_mask"][year_idx]
labels = tokenized["labels"][year_idx]
with torch.no_grad():
outputs = model(ids.unsqueeze(dim=0),
atten_mask.unsqueeze(dim=0))
preds = torch.argmax(outputs[0][0].cpu(), dim=1)
#was_masked = labels.cpu() != -100
preds = torch.where(is_masked, preds, -100)
p_female.append(
len(torch.where(preds == 0)[0]) / num_preds * 100)
p_male.append(
len(torch.where(preds == 1)[0]) / num_preds * 100)
dfs.append(pd.DataFrame(
{f"%f_{prefix}": p_female, f"%m_{prefix}": p_male}))
for bert_like in bert_like_models:
p_female = []
p_male = []
for year_idx in range(len(tokenized['ids'])):
toks = tokenized["toks"][year_idx]
target_text_for_bert = ' '.join(
toks[1:-1]) # Removing [CLS] and [SEP]
prefix = bert_like
model = models[(bert_like,)]
mask_filled_text = model(target_text_for_bert)
# Quick hack as realized return type based on how many MASKs in text.
if type(mask_filled_text[0]) is not list:
mask_filled_text = [mask_filled_text]
female_pronouns = [
1 if pronoun[0]["token_str"] in female_gendered_tokens else 0
for pronoun in mask_filled_text
]
male_pronouns = [
1 if pronoun[0]["token_str"] in male_gendered_tokens else 0
for pronoun in mask_filled_text
]
p_female.append(sum(female_pronouns) / num_preds * 100)
p_male.append(sum(male_pronouns) / num_preds * 100)
dfs.append(pd.DataFrame(
{f"%f_{prefix}": p_female, f"%m_{prefix}": p_male}))
# To display to user as an example
toks = tokenized["toks"][0]
target_text_w_masks = ' '.join(toks[1:-1])
results = pd.concat(dfs, axis=1).set_index("year")
female_df = results.filter(regex=".*f_").reset_index() # Gradio doesn't 'see' index?
female_df_for_plot = (
female_df
)
male_df = results.filter(regex=".*m_").reset_index() # Gradio doesn't 'see' index?
male_df_for_plot = (
male_df
)
return (
target_text_w_masks,
female_df_for_plot,
female_df,
male_df_for_plot,
male_df,
)
title = "Causing Gender Pronouns"
description = """
Intro
This is a demo for a project exploring possible spurious correlations that have been learned by our models. We first examined the training datasets and learning tasks to hypothesize what spurious correlations may exist. Below we can condition on these variables to determine what effect they may have on the prediction outcomes.
Specially in this demo: In a user provided sentence, with at least one reference to a `DATE` and one gender pronoun, we will see how sweeping through a range of `DATE` values can change the predicted pronouns. This effect can be observed in BERT base models and in our fine-tuned models (with a specific pronoun predicting task on the [wiki-bio](https://huggingface.co/datasets/wiki_bio) dataset).
One way to explain this phenomenon is by looking at a likely data generating process for biographical-like data in both the main BERT training dataset as well as the `wiki_bio` dataset, in the form of a causal DAG.
Causal DAG
In the DAG, we can see that `birth_place`, `birth_date` and `gender` are all independent elements that have no common cause with the other covariates in the DAG. However `birth_place`, `birth_date` and `gender` may all have a role in causing one's `access_to_resources`, with the general trend that `access_to_resources` has become less gender-dependent over time, but not in every `birth_place`, with recent events in Afghanistan providing a stark counterexample to this trend.
Importantly, `access_to_resources` determines how, **if at all**, you may appear in the dataset's `context_words`.
We argue that although there are complex causal interactions between each word in any given sentence, the `context_words` are more likely to cause the `gender_pronouns`, rather than vice versa. For example, if the subject is a famous doctor and the object is her wealthy father, these context words will determine which person is being referred to, and thus which gendered-pronoun to use.
In this graph, arrow heads are intended to show the assumed direction of causation. E.g. as described above, we are claiming `context_words` cause the `gender_pronouns`. While causation follow direction of the arrows, statistical correlation can flow in any direction (it is cause-agnostic).
In the case of this graph, any pink path between `context_words` and `gender_pronouns` will allow the flow of statistical correlation, inviting confounding and thus spurious correlations into the trained model.
Those familiar with causal DAGs may note when can simply condition on `gender` to block any confounding between the `context_words` and the `gender_pronouns`. However, this is not always possible, particularly in generative or mask-filling tasks where gender may be unknown, common in language modeling and in the demo below.
How to use this demo
In this demo, a user can add any sentence that contains at least one gender pronoun and the capitalized word `DATE`. We then sweep through a range of `date` values in the place of `DATE`, while masking (for prediction) the gender pronouns (included in the list below).
```
gendered_lists = [
['he', 'she'],
['him', 'her'],
['his', 'hers'],
['male', 'female'],
['man', 'woman'],
['men', 'women'],
["husband", "wife"],
]
```
In addition to choosing the test sentence, we ask that you pick how the fine-tuned model was trained:
- conditioning variable: which, if any, conditioning variable from the three noted above in the DAG, was included in the text at train time.
- loss function weight: weight assigned to the minority class (female pronouns in this fine-tuning dataset) that was included in the text at train time.
You can also optionally pick a bert-like model for comparison.
Some notes:
- Gradio currently only supports 6 plotting colors (but there are [plans](https://github.com/gradio-app/gradio/issues/1088) to support more!), so best to not select too many models at once for now.
- If the dataframes appear to not update with new fields, it may help to 'Clear' the fields before 'Submitting' new inputs.
What are the results
In the resulting plots, we can look for a dose-response relationship between:
- our treatment: the sample text,
- and our outcome: the predicted gender of pronouns in the text.
Specifically, we are seeing if 1) making larger magnitude intervention: an older `DATE` in the text will, 2) result in a larger magnitude effect in the outcome: higher percentage of predicted female pronouns.
Some trends that appear in the test sentences I have tried:
- Conditioning on `birth_date` metadata in both training and inference text has the largest dose-response relationship. This seems reasonable, as the fine-tuned model is able to 'stratify' a learned relationship between gender pronouns and dates, when both are present in the text.
- While conditioning on either no metadata or `birth_place` data training, have similar middle-ground effects for this inference task.
- Finally, conditioning on `name` metadata in training, (while again conditioning on `date` in inference) has almost no dose-response relationship. It appears the learning of a `name —> gender pronouns` relationship was sufficiently successful to overwhelm any potential more nuanced learning, such as that driven by `birth_date` or `place`.
Please feel free to ping me on the Hugging Face discord (I'm 'emily_learner' there), with any feedback/comments/concerns or interesting findings!
"""
article = "Check out [main colab notebook](https://colab.research.google.com/drive/14ce4KD6PrCIL60Eng-t79tEI1UP-DHGz?usp=sharing#scrollTo=Mg1tUeHLRLaG) \
with a lot more details about this method and implementation."
ceo_example = [
20,
["none", "birth_date", "name"],
FEMALE_WEIGHTS,
[],
'Born in DATE, she was a CEO. Her work was greatly respected, and she was well-regarded in her field.',
]
death_date_example = [
10,
['birth_date'],
[1.5],
BERT_LIKE_MODELS,
'Died in DATE, she was recognized for her great accomplishments to the field of teaching.'
]
no_job_example = [
20,
CONDITIONING_VARIABLES,
[1.5],
BERT_LIKE_MODELS,
'Born in DATE, she was a happy child. Her family raised her in a loving environment where she thrived.',
]
coder_example = [
20,
['none', 'birth_date'],
[1.5],
['bert'],
'Born in DATE, she was a computer scientist. Her work was greatly respected, and she was well-regarded in her field.'
]
gr.Interface(
fn=predict_gender_pronouns,
inputs=[
gr.inputs.Number(
default=20,
label="Number of points (years) plotted -- select fewer if slow.",
),
gr.inputs.CheckboxGroup(
CONDITIONING_VARIABLES,
default=["birth_date"],
type="value",
label="(1) Pick conditioning variable included in text during fine-tuning.",
),
gr.inputs.CheckboxGroup(
FEMALE_WEIGHTS,
default=[1.5],
type="value",
label="(2) Pick loss function weight placed on female predictions during fine-tuning.",
),
gr.inputs.CheckboxGroup(
BERT_LIKE_MODELS,
default=BERT_LIKE_MODELS,
type="value",
label="(Optional) Pick BERT-like base uncased model for comparison.",
),
gr.inputs.Textbox(
lines=7,
label="Input Text: Include one or more instance of the word 'DATE' below (to be replaced with a range of `{dates}` in demo), and one or more gender pronoun (to be `[MASK]`ed for prediction).",
default="She always walked past the building built in DATE on her way to her job as an elementary school teacher.",
),
],
outputs=[
gr.outputs.Textbox(
type="auto", label="Sample target text fed to model"),
gr.outputs.Timeseries(
x="year",
label="Precent pred female pronoun vs year, per model trained with conditioning and with weight for female preds",
),
gr.outputs.Dataframe(
overflow_row_behaviour="show_ends",
label="Precent pred female pronoun vs year, per model trained with conditioning and with weight for female preds",
),
gr.outputs.Timeseries(
x="year",
label="Precent pred male pronoun vs year, per model trained with conditioning and with weight for female preds",
),
gr.outputs.Dataframe(
overflow_row_behaviour="show_ends",
label="Precent pred male pronoun vs year, per model trained with conditioning and with weight for female preds",
),
],
title=title,
description=description,
article=article,
examples=[ceo_example, death_date_example, no_job_example, coder_example]
).launch()