Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
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+## This uploaded dataset is a sample data of 12k rows for train and 1.2k for test to get idea of the dataset used in making of PathoPreter 
+<a href="https://huggingface.co/YADAV0206/PathoPreter-4B-SNV-Pathogen-ClinVar-gnomAD">https://huggingface.co/YADAV0206/PathoPreter-4B-SNV-Pathogen-ClinVar-gnomAD</a>
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+The model's full training dataset contains approximately 144k pathogenic variants and 1.05 million benign variants, for a total of about 1.2 million samples and Testing have 55k total different samples with 11 seperate ablations tests (on same 55k rows) making around 12*55k=660k”
+
+To get the Dataset contact 
+**Rohit Yadav**  
+
+<div>
+<a href="yrohit1825@gmail.com">yrohit1825@gmail.com</a> | <a href="https://github.com/YADAV1825/PathoPreter">Github: https://github.com/YADAV1825/PathoPreter</a>
+</div>
+
+---
+
+## 📦 Dataset Availability
+
+<div>Dataset Construction and Availability:</div>
+The datasets used to train and evaluate PathoPreter (including large-scale ClinVar-derived SNV corpora, controlled ablation test suites, and robustness evaluation datasets) were fully constructed in-house using publicly available, permissively licensed genomic resources such as ClinVar and gnomAD. All upstream sources are properly credited and explicitly permit commercial use and redistribution.
+Significant original engineering and curation effort was applied beyond raw data usage. This included large-scale extraction, normalization, schema unification, quality control, deduplication, and strict train–test disjointness enforcement. Approximately 8 million raw ClinVar variants and ~250 GB of gnomAD VCF data were processed and merged into production-grade Parquet datasets optimized for large-scale analytics, machine learning training, and downstream integration.
+The end-to-end data construction process required approximately 150 hours of compute time and over 250 hours of expert engineering and curation work. The resulting datasets constitute a high-value derived data asset, distinct from the original source distributions.
+These datasets are available for licensed distribution to startups, enterprises, and research organizations for use in applied genomics, AI/ML model development, benchmarking, variant prioritization workflows, and internal research. Commercial licensing, redistribution terms, and support options are available upon request.
+
+
+Available components include:(in Parquet and CSV both)
+- Large-scale ClinVar-style SNV training dataset
+- Held-out test set with identical variants across ablations
+- Controlled ablation datasets (signal removal studies)
+- Fake-variant's robustness evaluation dataset (see below why is it important in FAKE VARIANT ROBUSTNESS TEST)
+- Balanced CSV subsets suitable for classical ML training
+- Data audit and leakage-verification scripts
+
+If you are interested in:
+- dataset licensing
+- research or industry use
+- collaboration or benchmarking
+- reproducing or extending this work
+
+please contact:
+
+**Rohit Yadav**  
+
+<div>
+<a href="yrohit1825@gmail.com">yrohit1825@gmail.com</a> | <a href="https://github.com/YADAV1825/PathoPreter">Github: https://github.com/YADAV1825/PathoPreter</a>
+</div>
+
+Requests are evaluated on a case-by-case basis.
+