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README.md

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+---
+language: en
+license: apache-2.0
+tags:
+  - pytorch
+  - text-classification
+  - dei
+  - bias-detection
+  - equibert
+datasets:
+  - synthetic
+metrics:
+  - f1
+  - roc_auc
+---
+
+# EquiBERT — Bias Classifier
+
+**Model ID:** `SallySims/equibert-bias-classifier`
+
+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 |
+| 3 | `ability_bias` | Ableist language, physical requirements, disability framing |
+| 4 | `socioeconomic_bias` | Class-coded language, credential gatekeeping |
+| 5 | `cultural_bias` | Cultural exclusion, religious insensitivity |
+| 6 | `intersectional` | Compounding bias across multiple identity dimensions |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("SallySims/equibert-bias-classifier")
+
+text = "We need a rock star developer who can dominate the roadmap."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+
+# Load model weights and run inference
+# (use with the EquiBERT modeling code from the repository)
+```
+
+## Task Head Architecture
+
+```
+CLS token → Dropout(0.1) → Linear(hidden, hidden//2) → GELU → Linear(hidden//2, 7)
+                                                                       ↓
+                                                          BCEWithLogitsLoss (multi-label)
+                                                          Sigmoid threshold @ 0.5
+```
+
+## Performance (synthetic data, seed=42)
+
+| Metric | Score |
+|--------|-------|
+| Macro F1 | 0.72 |
+| Micro F1 | 0.76 |
+| AUC | 0.81 |
+
+## 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](https://huggingface.co/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:
+
+```bibtex
+@misc{equibert2024,
+  author    = {SallySims},
+  title     = {EquiBERT: A Multi-Task DEI Transformer},
+  year      = {2024},
+  publisher = {HuggingFace},
+  url       = {https://huggingface.co/SallySims}
+}
+```
+
+## All EquiBERT Models
+
+| Model | Task | Primary Metric |
+|-------|------|---------------|
+| [equibert-bias-classifier](https://huggingface.co/SallySims/equibert-bias-classifier) | Bias Detection | Macro F1 |
+| [equibert-microaggression](https://huggingface.co/SallySims/equibert-microaggression) | Microaggression Detection | Macro F1 |
+| [equibert-category-tagger](https://huggingface.co/SallySims/equibert-category-tagger) | DEI Category Tagging | Macro F1 |
+| [equibert-event-exclusion](https://huggingface.co/SallySims/equibert-event-exclusion) | Event Exclusion Classification | Macro F1 |
+| [equibert-inclusive-language](https://huggingface.co/SallySims/equibert-inclusive-language) | Inclusive Language Scoring | Span F1 |
+| [equibert-review-auditor](https://huggingface.co/SallySims/equibert-review-auditor) | Performance Review Auditing | Span F1 |
+| [equibert-washing-detector](https://huggingface.co/SallySims/equibert-washing-detector) | DEI Washing Detection | MAE |
+| [equibert-framing-scorer](https://huggingface.co/SallySims/equibert-framing-scorer) | Report Framing Scoring | MAE |
+| [equibert-awareness-scorer](https://huggingface.co/SallySims/equibert-awareness-scorer) | DEI Awareness Scoring | MAE |
+| [equibert-similarity](https://huggingface.co/SallySims/equibert-similarity) | Semantic Similarity | Accuracy |
+| [equibert-ner](https://huggingface.co/SallySims/equibert-ner) | DEI Entity Recognition | Span F1 |
+| [equibert-relation-extraction](https://huggingface.co/SallySims/equibert-relation-extraction) | Relation Extraction | Macro F1 |
+| [equibert-qa](https://huggingface.co/SallySims/equibert-qa) | Extractive QA | Span EM |
+| [equibert-search](https://huggingface.co/SallySims/equibert-search) | Semantic Search | MRR@10 |
+| [equibert-nli](https://huggingface.co/SallySims/equibert-nli) | NLI / Textual Entailment | Macro F1 |
+| [equibert-generator](https://huggingface.co/SallySims/equibert-generator) | DEI Text Generation | ROUGE-L |