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README.md

@@ -1,36 +1,229 @@
----
-dataset_info:
-  features:
-  - name: task
-    dtype: string
-  - name: smiles
-    dtype: string
-  - name: cid
-    dtype: int64
-  - name: molecular_weight
-    dtype: float64
-  - name: logp
-    dtype: float64
-  - name: messages
-    list:
-    - name: content
-      dtype: string
-    - name: role
-      dtype: string
-  splits:
-  - name: train
-    num_bytes: 24616678
-    num_examples: 17995
-  - name: test
-    num_bytes: 2747075
-    num_examples: 2000
-  download_size: 24303243
-  dataset_size: 27363753
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
----
+---
+license: mit
+task_categories:
+- question-answering
+- text-generation
+language:
+- en
+tags:
+- chemistry
+- molecular-properties
+- drug-discovery
+- spectroscopy
+- safety-assessment
+- synthetic-chemistry
+- rlvr
+- reinforcement-learning
+- cheminformatics
+- rdkit
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.parquet
+  - split: test
+    path: test.parquet
+---
+
+# ChemBench-RLVR: Comprehensive Chemistry Dataset for Reinforcement Learning from Verifiable Rewards
+
+## Dataset Description
+
+ChemBench-RLVR is a high-quality, balanced dataset containing **19,995 question-answer pairs** across **15 chemistry task types**. This dataset is specifically designed for training language models using Reinforcement Learning from Verifiable Rewards (RLVR), where all answers are computationally verifiable using established cheminformatics tools.
+
+### Key Features
+
+- 🧪 **19,995 balanced QA pairs** across 15 chemistry domains
+- 🔬 **100% local calculations** - no external API dependencies  
+- ⚖️ **Perfect task balance** - each task has exactly 1,333 samples
+- 🎯 **Verifiable answers** - all responses computed using RDKit, spyrmsd, and other reliable tools
+- 📚 **Template diversity** - 3 prompt variations per task
+- 🌐 **Molecular diversity** - sourced from 20,000 PubChem compounds
+- 📦 **Multiple formats** - Available in both Parquet and JSONL formats
+
+## Dataset Statistics
+
+### Overview
+- **Total Samples**: 19,995
+- **Training Split**: 17,995 samples (90%)
+- **Test Split**: 2,000 samples (10%)
+- **Generation Time**: 107.7 seconds
+- **Average per Task**: 1,333 samples
+- **Zero Duplicates**: All QA pairs are unique
+- **Reproducible**: Fixed seed (42) for consistent results
+
+### Molecular Complexity Statistics
+
+- **SMILES Length**: 38.8 ± 16.0 characters (avg ± std)
+- **Min SMILES Length**: 2 characters
+- **Max SMILES Length**: 99 characters
+- **Median SMILES Length**: 37 characters
+
+### Text Length Statistics
+
+#### Questions
+- **Average Length**: 111 ± 18 characters
+- **Range**: 63-189 characters
+- **Median**: 110 characters
+
+#### Answers
+- **Average Length**: 1130 ± 3827 characters
+- **Range**: 1-27577 characters
+- **Median**: 75 characters
+
+## Task Distribution
+
+### Complete Task Breakdown
+- **Aromatic Ring Count**: 1,333 samples- **Bioactivity Prediction**: 1,333 samples- **Drug Likeness Assessment**: 1,333 samples- **Functional Group Identification**: 1,333 samples- **Ghs Hazard Statement Identification**: 1,333 samples- **Ghs Pictogram Identification**: 1,333 samples- **Hydrogen Bond Properties**: 1,333 samples- **Iupac Name Generation**: 1,333 samples- **Logp Calculation**: 1,333 samples- **Molecular Weight Calculation**: 1,333 samples- **Molecule Visualization**: 1,333 samples- **Reactivity Prediction**: 1,333 samples- **Solubility Prediction**: 1,333 samples- **Stereochemistry Analysis**: 1,333 samples- **Synthetic Accessibility**: 1,333 samples
+
+## Chemistry Task Categories
+
+### 🧪 Core Molecular Properties (6 tasks)
+- **Molecular Weight Calculation**: Exact molecular mass computation using RDKit
+- **LogP Calculation**: Octanol-water partition coefficient prediction  
+- **Aromatic Ring Count**: Identification of aromatic ring systems
+- **Hydrogen Bond Properties**: Count of donors and acceptors
+- **IUPAC Name Generation**: Systematic nomenclature from structure
+- **Molecule Visualization**: 2D structural diagram generation
+
+### 🔬 Advanced Spectroscopy & Structure (3 tasks)
+- **NMR Signal Prediction**: 1H and 13C chemical shift estimation via RDKit fallback methods
+- **Point Group Determination**: Molecular symmetry analysis using RDKit/spyrmsd
+- **Stereochemistry Analysis**: Chiral center identification and stereoisomer enumeration
+- **Functional Group Identification**: SMARTS-based substructure recognition
+
+### ⚠️ Safety & Hazard Assessment (2 tasks)
+- **GHS Pictogram Identification**: Hazard symbol classification from structure
+- **GHS Hazard Statement Identification**: H-code assignment using chemical patterns
+
+### 💊 Pharmaceutical Chemistry (4 tasks)
+- **Drug-Likeness Assessment**: Lipinski's Rule of Five evaluation
+- **Solubility Prediction**: Aqueous solubility estimation via group contribution
+- **Bioactivity Prediction**: Pharmacological class prediction from structural features
+- **Stereochemistry Analysis**: Chiral center identification and stereoisomer counting
+
+### ⚗️ Synthetic Chemistry (2 tasks)
+- **Synthetic Accessibility**: Complexity scoring for synthesis planning
+- **Reactivity Prediction**: Reactive site identification and charge analysis
+
+## Task Distribution
+
+- **Aromatic Ring Count**: 1,333 samples
+- **Bioactivity Prediction**: 1,333 samples
+- **Drug Likeness Assessment**: 1,333 samples
+- **Functional Group Identification**: 1,333 samples
+- **Ghs Hazard Statement Identification**: 1,333 samples
+- **Ghs Pictogram Identification**: 1,333 samples
+- **Hydrogen Bond Properties**: 1,333 samples
+- **Iupac Name Generation**: 1,333 samples
+- **Logp Calculation**: 1,333 samples
+- **Molecular Weight Calculation**: 1,333 samples
+- **Molecule Visualization**: 1,333 samples
+- **Reactivity Prediction**: 1,333 samples
+- **Solubility Prediction**: 1,333 samples
+- **Stereochemistry Analysis**: 1,333 samples
+- **Synthetic Accessibility**: 1,333 samples
+
+## Dataset Structure
+
+Each sample contains:
+- **messages**: List of conversation turns (user question, assistant answer)
+- **task**: Chemistry task category
+- **smiles**: SMILES string of the molecule
+- **difficulty**: Task difficulty level (easy/medium/hard)
+
+### Example Sample
+
+```json
+{
+  "messages": [
+    {
+      "role": "user", 
+      "content": "What is the molecular weight of the compound with SMILES 'CCO'?"
+    },
+    {
+      "role": "assistant",
+      "content": "The molecular weight of ethanol (CCO) is 46.07 g/mol."
+    }
+  ],
+  "task": "Molecular_Weight_Calculation",
+  "smiles": "CCO",
+  "difficulty": "easy"
+}
+```
+
+## Computational Methods
+
+All answers are computed using established cheminformatics libraries:
+
+- **RDKit**: Molecular property calculations, structure analysis
+- **spyrmsd**: Symmetry-corrected molecular analysis  
+- **MDAnalysis**: Molecular dynamics and structure processing
+- **PyTorch**: Neural network components (when available)
+
+## Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load full dataset
+dataset = load_dataset("summykai/chembench-rlvr-test")
+
+# Load specific split
+train_data = load_dataset("summykai/chembench-rlvr-test", split="train")
+test_data = load_dataset("summykai/chembench-rlvr-test", split="test")
+```
+
+### RLVR Training
+
+This dataset is optimized for Reinforcement Learning from Verifiable Rewards:
+
+```python
+# Example: Verify molecular weight calculation
+from rdkit import Chem
+from rdkit.Chem import Descriptors
+
+def verify_molecular_weight(smiles: str, predicted_mw: float) -> bool:
+    mol = Chem.MolFromSmiles(smiles)
+    if mol is None:
+        return False
+    actual_mw = Descriptors.MolWt(mol)
+    return abs(actual_mw - predicted_mw) < 0.1
+```
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{chembench_rlvr_2025,
+  title={ChemBench-RLVR: Comprehensive Chemistry Dataset for Reinforcement Learning from Verifiable Rewards},
+  author={ChemBench Team},
+  year={2025},
+  url={https://huggingface.co/datasets/summykai/chembench-rlvr-test},
+  note={Generated using RDKit, spyrmsd, and other open-source cheminformatics tools}
+}
+```
+
+## License
+
+This dataset is released under the MIT License. See LICENSE file for details.
+
+## Dataset Generation
+
+- **Generated on**: 2025-07-26 12:28:32 UTC
+- **Version**: 8.6-post8
+- **Seed**: 42 (for reproducibility)
+- **Source molecules**: PubChem compound database
+
+## Acknowledgments
+
+This dataset was generated using:
+- [RDKit](https://www.rdkit.org/) - Cheminformatics toolkit
+- [spyrmsd](https://github.com/RMeli/spyrmsd) - Symmetry-corrected RMSD calculations
+- [PubChem](https://pubchem.ncbi.nlm.nih.gov/) - Chemical compound database
+- [Hugging Face](https://huggingface.co/) - Dataset hosting and distribution