---
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.

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## 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 |

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## 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.

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## Intended Uses

- **LLM grounding & hallucination evaluation**
- Prompt engineering experiments
- Fine-tuning or zero/few-shot evaluation pipelines
- Data formatting utilities

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## 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.