docs(readme): fix wording, adding versions, consistency with GitHub readme

README.md

@@ -7,14 +7,14 @@ tags:
 pretty_name: ExomeBench
 size_categories:
 - 100K<n<1M
-license: cc
+license: cc-by-nc-4.0
 task_categories:
 - text-classification
 ---
 
 <br />
 <div align="center">
-  <h1 align="center">Exome Bench: A Benchmark for Clinical Variant Interpretation in Exome Regions 🧬</h1>
+  <h1 align="center">ExomeBench: A Benchmark for Clinical Variant Interpretation in Exome Regions 🧬</h1>
 
   <p align="center">
     <a href="https://www.researchsquare.com/article/rs-6115078/v1">
@@ -27,51 +27,50 @@ task_categories:
   </p>
 </div>
 
-The ExomeBench dataset is derived from ClinVar, a publicly accessible database maintained by the [National Center for Biotechnology Information (NCBI)](https://www.ncbi.nlm.nih.gov/clinvar). ClinVar provides comprehensive information on the clinical significance of genetic variants and their associations with human diseases. This dataset focuses on variants located in exome-specific regions and includes input sequences generated from the [Human Reference Genome (HRG)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.40/).
-
-This dataset provides a valuable resource for researchers and practitioners working on genetic variant analysis and its clinical implications. Exome-specific regions are critically important because they encompass all protein-coding regions of the genome, where disease-associated variants are most likely to occur. By focusing on exome-specific regions and using sequences from the Human Reference Genome, this dataset enables robust evaluation of models on clinically significant tasks.
-
-## Dataset Details
+## 1. Project Overview
 
+The **ExomeBench** dataset is derived from [ClinVar](https://www.ncbi.nlm.nih.gov/clinvar) [(March 2024 release)](https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/), a publicly accessible database maintained by the National Center for Biotechnology Information (NCBI). ClinVar provides comprehensive information on the clinical significance of genetic variants and their associations with human diseases. This dataset focuses on variants located in exome-specific regions and includes input sequences generated from the [Human Reference Genome (HRG, GRCh38)](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.40/).
 
-#### Data Collection
+This dataset provides a valuable resource for researchers and practitioners working on genetic variant analysis and its clinical implications. Exome-specific regions are critically important because they encompass all protein-coding regions of the genome, where disease-associated variants are most likely to occur. By focusing on exome-specific regions and using sequences from the Human Reference Genome, this dataset enables robust evaluation of models on clinically significant tasks.
 
-1. **Source**: Variants are sourced from the ClinVar database.
-2. **Clinical Significance**: ClinVar provides detailed information on the clinical significance of each variant and its association with human diseases.
 
-#### Data Filtering
+## 2. Dataset Details
 
-1. **Assertion Criteria**: We include only variants with at least one submitter providing an interpretation and satisfying the assertion criteria for reliability.  
-2. **Variant Type**: Only single-nucleotide variants (SNVs) are selected.  
-2. **Exome-Specific Regions**: Filter the variants to include only those located in exome-specific regions.
+### Data Collection
+- **Source**: Variants are sourced from the ClinVar database.
+- **Clinical Significance**: ClinVar provides detailed information on the clinical significance of each variant and its association with human diseases.
 
-#### Sequence Generation
+### Data Filtering
+- **Assertion Criteria**: We include only variants with at least one submitter providing an interpretation and satisfying the assertion criteria for reliability.  
+- **Variant Type**: Only single-nucleotide variants (SNVs) are selected.  
+- **Exome-Specific Regions**: Filter the variants to include only those located in exome-specific regions (GENCODE v.38).
 
-1. **Human Reference Genome (HRG)**: For each variant, generate input sequences from the HRG using genome.
-2. **Sequence Length**: The length of the sequences is a parameter, typically set to 100 base pairs (bp).
-3. **Variant Positioning**: The variant is centered within the sequence, which is read in from a FASTA file.
+### Sequence Generation
+- **Human Reference Genome (HRG, GRCh38)**: For each variant, generate input sequences from the HRG using genome.
+- **Sequence Length**: The length of the sequences is a parameter, typically set to 100 base pairs (bp).
+- **Variant Positioning**: The variant is centered within the sequence, which is read in from a FASTA file.
 
-#### Dataset Format
+### Dataset Format
 Each dataset entry consists of two main fields:
 
 - `sequence` (str): A DNA sequence centered around the variant.
 - `label` (int): Task-specific integer-encoded class index.
 
-### Tasks
-
-ExomeBench includes **five**, each framed as a classification problem:
+## 3. Tasks
+ExomeBench includes **five supervised tasks**, each framed as a classification problem:
 
-1. **Pathohenic Variant Prediction (PV)**  
+- **Pathogenic Variant Prediction (PV)**  
    Classify exome variants into four clinical significance categories: *pathogenic*, *likely pathogenic*, *likely benign*, or *benign*. Variants from the same gene are split across train/test to prevent leakage.
 
-2. **Phenotype Association**  
+- **Phenotype Association**  
    - **Cancer-Predisposing Syndrome (CPS)**: Determine if a variant is linked to Hereditary Cancer-Predisposing Syndrome.  
    - **Cardiovascular Phenotype (CP)**: Predict whether a variant is associated with cardiovascular conditions.
 
-3. **Gene Localization**  
+- **Gene Localization**  
    - **BRCA Classification (BRCA)**: Identify whether a variant belongs to *BRCA1*, *BRCA2*, or neither.  
    - **Top 5 Genes Prediction (TFG)**: Classify a variant into one of the five most frequently represented genes in the dataset.
 
+
 <div align="center">
 
 <table border="1" cellspacing="0" cellpadding="8">
@@ -89,7 +88,7 @@ ExomeBench includes **five**, each framed as a classification problem:
   </thead>
   <tbody>
     <tr>
-      <td>Pathohenic Variant Prediction (PV)</td>
+      <td>Pathogenic Variant Prediction (PV)</td>
       <td align="center">4</td>
       <td align="center">85503</td>
       <td align="center">9340</td>
@@ -128,22 +127,7 @@ ExomeBench includes **five**, each framed as a classification problem:
 
 </div>
 
-## Usage
-```python
-from datasets import load_dataset
-# One of:
-# [
-#  'brca',
-#  'cancer_predisposing_syndrome',
-#  'cardiovascular_phenotype',
-#  'pathogenic_variant',
-#  'top_five_genes'
-# ]
-task_name = "brca"
-dataset = load_dataset("cerebras/exome_bench", data_dir=task_name)
-# dataset['train'], dataset['validation'], dataset['test']
-```
-## SOTA Model Evaluations
+## 4. SOTA Model Performances
 
 <div align="center">
 
@@ -176,15 +160,32 @@ dataset = load_dataset("cerebras/exome_bench", data_dir=task_name)
 </div>
 
 
+## 5. Usage
+```python
+from datasets import load_dataset
+# One of:
+# [
+#  'brca',
+#  'cancer_predisposing_syndrome',
+#  'cardiovascular_phenotype',
+#  'pathogenic_variant',
+#  'top_five_genes'
+# ]
+task_name = "brca"
+dataset = load_dataset("cerebras/exome_bench", data_dir=task_name)
+# dataset['train'], dataset['validation'], dataset['test']
+```
+
+
 ## Citation
 This benchmark was developed as part of the efforts supporting the paper:
-`Introducing STRAND: A Foundational Sequence Transformer for Range Adaptive Nucleotide Decoding` in collabration with [Mayo Clinic](https://www.mayoclinic.org/).
+`Introducing STRAND: A Foundational Sequence Transformer for Range Adaptive Nucleotide Decoding` in collaboration with [Mayo Clinic](https://www.mayoclinic.org/).
 If you find our work valuable, please consider giving the project a star and citing it in your research:
 
 ```bib
 @article{ExomeBench, 
     DOI={10.2139/ssrn.5183178}, 
-    title={Introducing strand: A foundational sequence transformer for range adaptive nucleotide decoding}, 
+    title={Introducing STRAND: A Foundational Sequence Transformer for Range Adaptive Nucleotide Decoding}, 
     author={Ayanian, Shant and Osborne, Andrew and Xu, Clark and Molnar, Carl and Das, Pravat and Perez, Xoab and Natalia, Vassilieva and Pondenkandath, Vinay and Kanakiya, Bhargav and Venkatesh, Ganesh and et al.}, 
     year={2025},
     journal={Research Square Pre-print}
@@ -206,8 +207,8 @@ The dataset is for research and benchmarking only; it should not be used as a st
 
 ## Dataset Card Contact
 
-Correspanding email: exome-bench@cerebras.net
+Corresponding email: exome-bench@cerebras.net
 
-- **Curated by:** Cerebras Systems in collabration with Mayo Clinics
+- **Curated by:** Cerebras Systems in collaboration with Mayo Clinic
 - **Language(s) (NLP):** Python
-- **License:** Creative Commons Attribution 4.0: `cc-by-4.0`
+- **License:** Creative Commons Attribution-NonCommercial 4.0 International: `cc-by-nc-4.0`