Update README.md

README.md

@@ -1,3 +1,109 @@
----
-license: cc-by-nc-4.0
----
+---
+datasets:
+- nuhuibrahim/recifinegold
+language:
+- en
+base_model:
+- FacebookAI/roberta-base
+license: cc-by-nc-4.0
+tags:
+  - token-classification
+  - named-entity-recognition
+  - roberta
+  - recipes
+  - knowledge-augmented
+  - entity-conditioned
+---
+
+# ReciFineGold RecipeRoBERTa (Traditional Token Classification) `recifinegold-reciperoberta-trad`
+
+This model is a **RoBERTa-base** token-classification model trained on **ReciFineGold** for **recipe-focused Named Entity Recognition (NER)**.
+
+## What this checkpoint is
+
+- **Backbone:** RoBERTa-base
+- **Task:** Token classification (BIO-style tagging)
+
+## How to use
+
+The ReciFine library provides a lightweight inference wrapper (`ReciFineNER`) that handles extraction and decoding.
+
+```python
+pip install https://github.com/nuhu-ibrahim/ReciFine/archive/refs/tags/V1.zip
+
+from recifine.inferencing.inference import ReciFineNER
+
+ner = ReciFineNER.from_pretrained(
+    model="reciperoberta",
+    task_formulation="traditional",
+)
+
+text = "Add 2 cups of chopped onions and fry until golden ."
+
+prediction = ner.process_text(
+    text
+)
+
+print(prediction)
+```
+
+## Quick links (documentation + notebook)
+
+- **ReciFine library (recommended):** training scripts, inference wrappers, and full documentation  
+  https://github.com/nuhu-ibrahim/ReciFine/tree/main
+
+- **Colab (end-to-end usage demo):**  
+  https://colab.research.google.com/drive/1CatH2YOhnOWf-VglprEgxONWRkrXRBXo?usp=sharing&authuser=1
+
+## Intended use
+
+Use this model to extract **fine-grained recipe entities** from procedural recipe text (e.g., instructions), including ingredients, tools, quantities, durations, actions, and state descriptors.
+
+Typical applications:
+- Structured parsing of recipe steps
+- Ingredient and action extraction for downstream cooking assistants
+- Data normalisation and indexing for recipe search
+- Entity-aware prompting and evaluation pipelines for recipe generation
+
+### Supported entity types
+
+| Entity Type | Definition |
+|---|---|
+| `FOOD` | Edible items, including both raw ingredients and intermediate products |
+| `TOOL` | Cooking tools such as *knives, bowls, pans* |
+| `DURATION` | Time durations in cooking (e.g., *20 minutes*) |
+| `QUANTITY` | Quantities associated with ingredients |
+| `ACTION_BY_CHEF` | Verbs for deliberate cook actions (e.g., *bring* in “Bring the mixture to a boil”) |
+| `ACTION_BY_CHEF_DISCONTINUOUS` | Non-contiguous parts of compound chef actions (e.g., *to a boil*) |
+| `ACTION_BY_FOOD` | Verbs where food is the agent (e.g., *melt, boil*) |
+| `ACTION_BY_TOOL` | Verbs denoting tool actions (e.g., *grind, beat*) |
+| `FOOD_STATE` | Descriptions of food’s state (e.g., *chopped, soft*) |
+| `TOOL_STATE` | Descriptions of tool readiness (e.g., *preheated, greased, covered*) |
+
+## Research Papers
+
+### Knowledge-Augmented and Entity Type-Specific Token Classification
+The knowledge-augmented and entity type-specific token classification model architecture is described in the paper 
+**Knowledge Augmentation Enhances Token Classification for Recipe Understanding**.
+
+```bibtex
+@inproceedings{
+  title     = {Knowledge Augmentation Enhances Token Classification for Recipe Understanding},
+  author    = {Ibrahim, Nuhu and Stevens, Robert and Batista-Navarro, Riza},
+  booktitle = {EACL},
+  year      = {2026}
+}
+```
+
+### ReciFine Datasets and Controllable Recipe Generation
+The ReciFine, ReciFineGold and ReciFineGen datasets are described in the paper
+**ReciFine: Finely Annotated Recipe Dataset for Controllable Recipe Generation**.
+
+```bibtex
+@inproceedings{
+  title   = {ReciFine: Finely Annotated Recipe Dataset for Controllable Recipe Generation},
+  author  = {Ibrahim, Nuhu and Ravikumar, Rishi and Stevens, Robert and Batista-Navarro, Riza},
+  booktitle = {EACL},
+  year    = {2026}
+}
+```