Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+---
+---
+dataset_name: facts_grounding_processed
+license: apache-2.0
+tags:
+- factuality
+- grounding
+- llm-evaluation
+- text
+- preprocessing
+---
+
+# Facts Grounding Processed
+
+This dataset is a **processed and reformatted version** of [`google/FACTS-grounding-public`](https://huggingface.co/datasets/google/FACTS-grounding-public), designed for **LLM factuality and grounding evaluation**.  
+It has been cleaned, enriched with additional metadata, and split into **train** and **validation** sets.
+
+---
+
+## Dataset Summary
+
+The dataset contains prompts, context documents, and target answers that challenge models to stay grounded in provided context rather than hallucinating.  
+Processing steps added extra features like:
+- `prompt` – consolidated instruction + user request + context
+- `has_url_in_context` – boolean flag for URLs in context
+- `len_system`, `len_user`, `len_context` – token/word length statistics
+- `row_id` – unique identifier for tracking
+
+---
+
+## Dataset Structure
+
+**Splits:**
+- **train** – 688 rows
+- **validation** – 172 rows
+
+**Features:**
+| Feature               | Type    | Description |
+|-----------------------|---------|-------------|
+| `system_instruction`  | string  | System-level instruction template |
+| `user_request`        | string  | User query or prompt |
+| `context_document`    | string  | Provided factual context |
+| `full_prompt`         | string  | Original concatenated instruction + context |
+| `prompt`              | string  | Processed consolidated prompt |
+| `has_url_in_context`  | bool    | Whether the context contains a URL |
+| `len_system`          | int64   | Length of system instruction |
+| `len_user`            | int64   | Length of user request |
+| `len_context`         | int64   | Length of context document |
+| `target`              | string  | Grounded factual answer |
+| `row_id`              | int64   | Unique row identifier |
+
+---
+
+## Processing
+
+The preprocessing was done in Python with the Hugging Face `datasets` library:
+1. Loaded the original dataset (`google/FACTS-grounding-public`)
+2. Added custom features (URL detection, length counts)
+3. Generated combined prompt field
+4. Split into train (80%) and validation (20%)
+5. Exported as Arrow and JSONL formats
+
+The preprocessing script is included in the repository for reproducibility.
+
+---
+
+## Intended Uses
+
+- **LLM grounding & hallucination evaluation**
+- Prompt engineering experiments
+- Fine-tuning or zero/few-shot evaluation pipelines
+- Data formatting utilities
+
+---
+
+## How to Use
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("GenAIDevTOProd/facts_grounding_processed")
+
+print(dataset["train"][0])
+
+License: 
+This processed dataset is licensed under Apache 2.0.
+The original data source is google/FACTS-grounding-public.
+