equibert-nli

EquiBERT — DEI Natural Language Inference

Model ID: SallySims/equibert-nli

DEI-domain NLI model for textual entailment and zero-shot classification. Drop-in replacement for facebook/bart-large-mnli.

Labels

Usage — Zero-Shot Classification

from transformers import pipeline

nli = pipeline("zero-shot-classification", model="SallySims/equibert-nli")

result = nli(
    "We conduct annual pay equity reviews published in our DEI report.",
    candidate_labels=["pay equity", "hiring bias", "inclusion culture"]
)
# {"labels": ["pay equity", ...], "scores": [0.91, ...]}

Usage — Direct Entailment

premise    = "We conduct annual pay equity reviews."
hypothesis = "The organisation has a formal pay equity process."
inputs = tokenizer(premise, hypothesis, return_tensors="pt")
# label = id2label[model(**inputs).logits.argmax(-1).item()]
# → "entailment"

DEI Policy Verification

Use this model to verify whether organisational statements are consistent with stated DEI policies — identifying contradictions between policy documents and actual communications.

Model Description

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: SallySims Framework: 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:

@misc{equibert2024,
  author    = {SallySims},
  title     = {EquiBERT: A Multi-Task DEI Transformer},
  year      = {2024},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/SallySims}
}

All EquiBERT Models