update readme.md

README.md

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 ---
 license: mit
+task_categories:
+- text-classification
+- tabular-classification
+- tabular-regression
+- question-answering
+language:
+- en
+tags:
+- rag
+- hallucination-detection
+- llm-evaluation
+- tabular-nlp
+- transformers
+pretty_name: RAG Hallucination Benchmark
+size_categories:
+- 10K<n<100K
 ---
+
+# RAG Hallucination Benchmark
+
+### Context
+Retrieval-Augmented Generation (RAG) is the industry standard for reducing LLM hallucinations, but detecting when a RAG system fails is a massive challenge. Most existing benchmarks focus only on massive Deep Learning models and lack tabular features. 
+This dataset provides a clean, engineered setup to train models (from XGBoost to RoBERTa) to detect hallucinations, predict context faithfulness, and measure answer relevance.
+
+### Content
+The dataset contains **30,000 synthetic RAG interactions** covering:
+
+* **Text Data:** User Prompts, Retrieved Contexts, and LLM Responses.
+* **Metadata:** Model Names, Temperature Settings, and Vector Similarity Scores.
+* **Engineered Features:** Lexical Overlap, Entity Match Scores, and Complexity Indices.
+* **Targets:**
+  * Binary Hallucination flags
+  * Multiclass Hallucination Types
+  * Regression targets for Faithfulness
+
+### Inspiration
+This was inspired by the need for a practical ML benchmark where you can practice both feature engineering for tabular models (LightGBM, XGBoost) and text classification for NLP Transformers (BERT, DeBERTa) on the same dataset.
+
+### Quick Load
+
+```python
+import pandas as pd
+
+url = "https://huggingface.co/datasets/vkshdev/rag-hallucination-benchmark/raw/main/RAG_context_adherence_and_hallucination_benchmark.csv"
+df = pd.read_csv(url)
+
+print(df.head())