SallySims
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equibert-inclusive-language

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 ---
+language: en
+license: apache-2.0
+tags:
+  - pytorch
+  - token-classification
+  - dei
+  - inclusive-language
+  - equibert
+metrics:
+  - f1
 ---
+# EquiBERT — Inclusive Language Scorer
+**Model ID:** `SallySims/equibert-inclusive-language`
+Dual-output model for inclusive language analysis:
+1. **Token-level BIO tagging** — identifies and spans problematic language
+2. **Document-level score** — 0.0 (highly exclusive) to 1.0 (highly inclusive)
+## Span Tag Labels (13 classes)
+`O`, `B-GENDERED`, `I-GENDERED`, `B-ABLEIST`, `I-ABLEIST`,
+`B-RACIAL_CODE`, `I-RACIAL_CODE`, `B-AGE_CODED`, `I-AGE_CODED`,
+`B-EXCLUSIONARY`, `I-EXCLUSIONARY`, `B-AGGRESSIVE`, `I-AGGRESSIVE`
+## Score Interpretation
+| Score | Grade | Meaning |
+|-------|-------|---------|
+| 0.9–1.0 | A | Highly inclusive |
+| 0.7–0.9 | B | Mostly inclusive, minor issues |
+| 0.5–0.7 | C | Moderate issues, revision recommended |
+| 0.3–0.5 | D | Significant issues, revision required |
+| 0.0–0.3 | F | Highly exclusive, major revision required |
+## Usage
+```python
+text = "We need a rock star developer who can dominate the roadmap."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+# outputs      = model(**inputs)
+# span_tags    = outputs.logits.argmax(-1)       # token-level BIO tags
+# incl_score   = outputs.hidden_states[0]         # 0-1 document score
+```
+## 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 |