https://github.com/tolga-b/debiaswe
Remove problematic gender bias from word embeddings.
https://github.com/tolga-b/debiaswe
debias gender-equality nips-2016 social-justice word-embeddings word2vec
Last synced: 7 months ago
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Remove problematic gender bias from word embeddings.
- Host: GitHub
- URL: https://github.com/tolga-b/debiaswe
- Owner: tolga-b
- License: mit
- Created: 2016-12-25T17:43:06.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2023-05-09T14:46:28.000Z (over 2 years ago)
- Last Synced: 2025-03-31T09:08:51.700Z (7 months ago)
- Topics: debias, gender-equality, nips-2016, social-justice, word-embeddings, word2vec
- Language: Jupyter Notebook
- Homepage: https://arxiv.org/abs/1607.06520
- Size: 58.6 KB
- Stars: 246
- Watchers: 7
- Forks: 90
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Debiaswe: try to make word embeddings less sexist
🔴[FAT* 2018 tutorial slides](https://drive.google.com/file/d/1IxIdmreH4qVYnx68QVkqCC9-_yyksoxR/view?usp=sharing)
Here we have the code and data for the following paper:
[Man is to Computer Programmer as Woman is to
Homemaker? Debiasing Word Embeddings](http://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings.pdf) by
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. Proceedings of [NIPS 2016](https://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings).
**Just looking to download a debiased embedding?**
You can download [binary](https://drive.google.com/file/d/0B5vZVlu2WoS5ZTBSekpUX0RSNDg/view?usp=sharing&resourcekey=0-qO1UY06KB42G1T6IeJ2XCQ)/[txt](https://drive.google.com/file/d/1_PvT4ZvtZjhq4HPywA8-u06epht9ccOw/view?usp=sharing) hard debiased version of the Google's Word2Vec embedding trained on Google News (Origin: GoogleNews-vectors-negative300.bin.gz found [here](https://code.google.com/archive/p/word2vec/)).
**Python scripts:**
- **learn_gender_specific.py**: given a word embedding and a seed set of gender-specific words (like king, she, etc.), it learns a much larger list of gender-specific words
- **debias.py**: given a word embedding, sets of gender-pairs, gender-specific words, and pairs to equalize, it outputs a new word embedding. This version basically reads/writes word2vec binary file format.
```
python learn_gender_specific.py ../embeddings/GoogleNews-vectors-negative300.bin 50000 ../data/gender_specific_seed.json gender_specific_full.json
```
```
python debias.py ../embeddings/GoogleNews-vectors-negative300.bin ../data/definitional_pairs.json ../data/gender_specific_full.json ../data/equalize_pairs.json ../embeddings/GoogleNews-vectors-negative300-hard-debiased.bin
```
We also have seed data used to debias and crowd data used to evaluate the embeddings.
**Data files:**
- **gender_specific_seed.json**: A list of 218 gender-specific words
- **gender_specific_full.json**: A list of 1441 gender-specific words
- **definitional_pairs.json**: The ten pairs of words we use to define the gender direction
- **equalize_pairs.json**: Some crowdsourced F-M pairs of words that represent gender direction
(All external files that I refer within this repo can be found in [this folder](https://drive.google.com/drive/folders/0B5vZVlu2WoS5dkRFY19YUXVIU2M?resourcekey=0-rZ1HR4Fb0XCi4HFUERGhRA&usp=sharing).)