Multi-label classifier that detects seven types of bias in
organisational text — job descriptions, HR communications,
policies, and workplace language.
Labels
ID
Label
Description
0
gender_bias
Gendered language, role assumptions, masculine-coded words
1
racial_bias
Racial coding, cultural fit language, tokenism
2
age_bias
Digital native language, overqualified framing, generational stereotypes
Compounding bias across multiple identity dimensions
Usage
python
1from transformers import AutoTokenizer
2import torch
34tokenizer = AutoTokenizer.from_pretrained("SallySims/equibert-bias-classifier")56text ="We need a rock star developer who can dominate the roadmap."7inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)89# Load model weights and run inference10# (use with the EquiBERT modeling code from the repository)
EquiBERT is a multi-task DEI (Diversity, Equity and Inclusion) transformer
built on a dual-encoder backbone that fuses RoBERTa-base and
DeBERTa-v3-base via a learned weighted sum (α parameter).
The fused representation is fed into task-specific heads covering
17 distinct DEI analysis tasks.
Organisation:SallySimsFramework: PyTorch + HuggingFace Transformers
Backbone: RoBERTa-base + DeBERTa-v3-base (dual encoder, fused)
Language: English
Domain: Organisational DEI text — HR communications, policies,
job descriptions, performance reviews, leadership statements, reports
Architecture
Input Text
│
├──▶ RoBERTa-base encoder ──▶ Linear projection
│ │
└──▶ DeBERTa-v3-base encoder ──▶ Linear projection
│
Weighted fusion (learned α)
│
Layer Norm + Dropout
│
Task-specific head (see below)
Training Data
Trained on synthetic DEI organisational text generated by the
EquiBERT synthetic data pipeline, covering 20 DEI categories
across HR, policy, leadership, and workforce analytics domains.
For production use, fine-tune on real labelled DEI data.
Limitations
Trained on synthetic data — predictions should be validated
before use in real HR or policy decisions.
English-only.
Not a substitute for qualified DEI practitioners or legal advice.
May reflect biases present in the training corpus.
Citation
If you use EquiBERT in your research, please cite:
bibtex
1@misc{equibert2024,
2 author = {SallySims},
3 title = {EquiBERT: A Multi-Task DEI Transformer},
4 year = {2024},
5 publisher = {HuggingFace},
6 url = {https://huggingface.co/SallySims}
7}
