CounterGen is a framework for generating counterfactual datasets, evaluating NLP models, and editing models to reduce bias. It provides powerful defaults, while offering simple ways to use your own data, data augmentation techniques, models, and evaluation metrics.

• countergen is a lightweight Python module which helps you generate counterfactual datasets and evaluate bias of models available locally or through an API. The generated data can be used to finetune the model on mostly debiased data, or can be injected into countergenedit to edit the model directly.
• countergenedit is a Python module which adds methods to easily evaluate PyTorch text generation models as well as text classifiers. It provides tools to analyze model activation and edit model to reduce bias.

Read the docs here: https://fabienroger.github.io/Countergen/

import countergen
augmented_ds = countergen.AugmentedDataset.from_default("male-stereotypes")
api_model = countergen.api_to_generative_model("davinci") # Evaluate GPT-3
model_evaluator = countergen.get_generative_model_evaluator(api_model)
countergen.evaluate_and_print(augmented_ds.samples, model_evaluator)

(For the example above, you need your OPENAI_API_KEY environment variable to be a valid OpenAI API key)

import countergen
ds = countergen.Dataset.from_jsonl("my_data.jsonl")
augmenters = [countergen.SimpleAugmenter.from_default("gender")]
augmented_ds = ds.augment(augmenters)
augmented_ds.save_to_jsonl("my_data_augmented.jsonl")

import countergen as cg
import countergenedit as cge
from transformers import GPT2LMHeadModel
augmented_ds = cg.AugmentedDataset.from_default("male-stereotypes")
model = GPT2LMHeadModel.from_pretrained("gpt2")
layers = cge.get_mlp_modules(model, [2, 3])
activation_ds = cge.ActivationsDataset.from_augmented_samples(
  augmented_ds.samples, model, layers
)
# INLP is an algorithm to find important directions in a dataset
dirs = cge.inlp(activation_ds)
configs = cge.get_edit_configs(layers, dirs)
new_model = cge.edit_model(model, configs=configs)

• LLMD (Fryer, 2022), to augment data using large language models;
• INLP (Ravfogel, 2020) and RLACE (Ravfogel, 2022), to find key directions in neural activations;
• Stereoset (Nadeem, 2020), a large collection of stereotypes;
• The "Double bind experiment" (Heilman, 2007), an experiment about bias in humans which can also be conducted with large language models, and (May, 2019), which provides the exact data we use;
• OpenAI's API, to run inferences on large languages models;
• De Gibert, 2018, which provides data about hate speech;
• nltk, and Jörg Michael's gender.c, which contain datasets about the gender and origin of first names;
• BigBench's Social Bias from Sentence Probability, which provides evaluation data and metrics.