Add detailed model card

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+  - pytorch
+  - text-classification
+  - nli
+  - zero-shot-classification
+  - dei
+  - equibert
+metrics:
+  - f1
+  - accuracy
+---
+
+# 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
+
+| ID | Label | Description |
+|----|-------|-------------|
+| 0 | `entailment` | Premise logically supports hypothesis |
+| 1 | `contradiction` | Premise contradicts hypothesis |
+| 2 | `neutral` | Premise neither supports nor contradicts |
+
+## Usage — Zero-Shot Classification
+
+```python
+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
+
+```python
+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](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 |