SallySims
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equibert-relation-extraction

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 ---
+language: en
+license: apache-2.0
+tags:
+  - pytorch
+  - text-classification
+  - relation-extraction
+  - dei
+  - equibert
+metrics:
+  - f1
+  - accuracy
 ---
+# EquiBERT — DEI Relation Extraction
+**Model ID:** `SallySims/equibert-relation-extraction`
+Extracts typed relations between DEI entities using entity markers.
+Input format: `[E1] subject [/E1] ... [E2] object [/E2]`
+## Relation Types (12)
+| Relation | Description | Example |
+|----------|-------------|---------|
+| `NO_RELATION` | No meaningful relation | — |
+| `EXCLUDES` | Entity excludes another | Manager EXCLUDES BIPOC employees |
+| `DISCRIMINATES` | Discriminatory act | Policy DISCRIMINATES AGAINST disabled staff |
+| `ADVANTAGES` | Provides advantage | Programme ADVANTAGES white candidates |
+| `DISADVANTAGES` | Creates disadvantage | Process DISADVANTAGES women |
+| `ACCOUNTABLE_FOR` | Holds accountability | CHRO ACCOUNTABLE_FOR pay equity |
+| `ADDRESSES` | Addresses an issue | Training ADDRESSES unconscious bias |
+| `VIOLATES` | Policy violation | Screening VIOLATES anti-discrimination policy |
+| `BENEFITS` | Provides benefit | ERG BENEFITS LGBTQ+ employees |
+| `HARMS` | Causes harm | Language HARMS neurodiverse candidates |
+| `REPRESENTS` | Representation claim | Board REPRESENTS diverse community |
+| `REPORTS_ON` | Reporting relation | Annual report REPORTS_ON pay gap |
+## Usage
+```python
+text = "[E1] manager [/E1] excluded [E2] BIPOC employees [/E2] from the workshop."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
+# relation = id2label[model(**inputs).logits.argmax(-1).item()]
+```
+## 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 |