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README.md

@@ -3,7 +3,6 @@ license: apache-2.0
 task_categories:
   - text-classification
   - token-classification
-  - text-generation
 language:
   - en
 tags:
@@ -12,196 +11,63 @@ tags:
   - kluyveromyces-marxianus
   - biobert
   - functional-genomics
-  - pangenome
   - yeast
-  - biomistral
 size_categories:
   - 10K<n<100K
 ---
 
-# 🧬 Kluyveromyces marxianus - BioBERT-Optimized Genomics Dataset
-
-<div align="center">
-  <img src="https://img.shields.io/badge/BioBERT-Optimized-green" alt="BioBERT"/>
-  <img src="https://img.shields.io/badge/Chunks-2-brightgreen" alt="Chunks"/>
-  <img src="https://img.shields.io/badge/Quality-0.855-yellow" alt="Quality"/>
-  <img src="https://img.shields.io/badge/512_tokens-Ready-blue" alt="512 tokens"/>
-</div>
-
-## 📋 Overview
-
-**2 semantically-optimized chunks** for BioBERT fine-tuning, processed with **BiOMistral-7B** quantum chunking engine.
-
-### Key Features
-
-✅ **Deep Semantic Coherence** (avg: 1.000)
-✅ **Optimal Token Size** (~512 tokens per chunk)
-✅ **Quality Filtering** (2 high-quality chunks)
-✅ **Entity Extraction** (genes, proteins, pathways, conditions)
-✅ **BioBERT-Ready** (1 chunks ≤512 tokens)
-
-## 🎯 Research Context
-
-**PhD Thesis**: *Functional Genomics of Robust Linear Yeasts (Kluyveromyces marxianus) using BioBERT and Pangenome Methodology for Identifying Key Survival Genes in Severe Gut Conditions*
-
-### Applications
-
-- 🧬 Gene survival mechanism discovery
-- 🔬 Metabolic pathway analysis
-- 🦠 Stress response characterization
-- 📊 Pangenome comparative genomics
-- 🤖 AI-driven gene function prediction
-
-## 📊 Dataset Statistics
-
-| Metric | Value |
-|--------|-------|
-| **Total Chunks** | 2 |
-| **High-Quality (≥0.7)** | 2 (100.0%) |
-| **BioBERT-Ready** | 1 (50.0%) |
-| **Avg Coherence** | 1.0000 |
-| **Avg Density** | 0.7108 |
-| **Avg Quality** | 0.8554 |
-| **Processing Date** | 2025-11-01T08:35:37.784389 |
-
-## 🧬 Data Structure
-
-```json
-{
-  "chunk_id": "train_0_0",
-  "global_id": 0,
-  "text": "Optimized text content...",
-  "token_count": 487,
-  "semantic_coherence": 0.8234,
-  "information_density": 0.7156,
-  "quality_score": 0.7695,
-  "entities": {
-    "genes": ["ABC1", "XYZ2"],
-    "proteins": ["hexokinase"],
-    "pathways": ["glycolysis"],
-    "organisms": ["marxianus"],
-    "conditions": ["acid", "gut"]
-  },
-  "biobert_ready": true
-}
-```
+# 🧬 Kluyveromyces marxianus - BioBERT-Optimized Chunks
 
-## 📁 Files
+**PhD Research Dataset**: Functional Genomics of Robust Linear Yeasts
 
-- `chunks_complete.json` - All chunks with metadata (2 chunks)
-- `chunks_high_quality.json` - Premium subset (2 chunks)
-- `chunks.jsonl` - Streaming format
-- `statistics.json` - Processing statistics
-- `analysis_dashboard.png` - Visual analytics
-- `interactive_dashboard.html` - Interactive Plotly dashboard
-- `entities_wordcloud.png` - Entity visualization
+## Overview
 
-## 🚀 Quick Start
+Semantically-optimized 512-token chunks for BioBERT fine-tuning, processed with BiOMistral-7B.
 
-### Load Dataset
+## Dataset Info
 
-```python
-import json
+- **Processing Date**: 2025-11-01
+- **Target Model**: BioBERT-Large v1.1
+- **Chunk Size**: 512 tokens (optimal)
+- **Source**: [Milad96/Kluyveromyces-marxianus](https://huggingface.co/datasets/Milad96/Kluyveromyces-marxianus)
 
-# High-quality subset
-with open('chunks_high_quality.json', 'r') as f:
-    chunks = json.load(f)
+## Features
 
-print(f"Loaded {len(chunks):,} high-quality chunks")
-```
+- Deep semantic coherence analysis
+- Automatic genomic entity extraction
+- Quality scoring and filtering
+- BioBERT-ready format
 
-### BioBERT Fine-tuning
+## Usage
 
 ```python
-from transformers import BertTokenizer, BertForSequenceClassification
-
-model = BertForSequenceClassification.from_pretrained(
-    "dmis-lab/biobert-large-cased-v1.1",
-    num_labels=YOUR_CLASSES
-)
-
-tokenizer = BertTokenizer.from_pretrained(
-    "dmis-lab/biobert-large-cased-v1.1"
-)
-
-# Chunks are already 512-token optimized!
-for chunk in chunks:
-    encoding = tokenizer(
-        chunk['text'],
-        truncation=True,
-        max_length=512,
-        padding='max_length',
-        return_tensors='pt'
-    )
-    # Train your model...
-```
+from datasets import load_dataset
+import json
 
-### Filter by Quality
+# Load high-quality chunks
+with open('chunks_high_quality.json', 'r') as f:
+    chunks = json.load(f)
 
-```python
-# Get excellent chunks (≥0.8)
-excellent = [c for c in chunks if c['quality_score'] >= 0.8]
-
-# Get gut-stress related chunks
-gut_chunks = [
-    c for c in chunks
-    if any(cond in c['entities']['conditions']
-           for cond in ['gut', 'acid', 'bile'])
-]
+# Each chunk has:
+# - text: optimized content
+# - token_count: ~512
+# - semantic_coherence: 0-1
+# - quality_score: 0-1
+# - entities: {genes, proteins, pathways}
 ```
 
-## 📈 Quality Metrics
-
-**Semantic Coherence** (0-1): Thematic consistency using BiOMistral embeddings
-**Information Density** (0-1): Vocabulary diversity (unique/total words)
-**Quality Score** (0-1): Combined metric `(coherence + density) / 2`
-
-### Quality Tiers
-
-- **Excellent** (≥0.8): Highly focused, rich content
-- **Good** (0.7-0.8): Strong coherence and diversity
-- **Acceptable** (0.5-0.7): Moderate quality
-- **Low** (<0.5): Basic content
-
-## 🔬 Processing Pipeline
-
-1. **BiOMistral-7B Loading** - 4-bit/FP16 quantization
-2. **Semantic Analysis** - Deep understanding via embeddings
-3. **Intelligent Chunking** - Boundary detection at natural breaks
-4. **Entity Extraction** - Automatic identification
-5. **Quality Scoring** - Multi-metric assessment
-6. **Visualization** - Interactive and static analytics
-
-## 🎓 Citation
+## Citation
 
 ```bibtex
 @dataset{kmx_chunks_2024,
-  title = {Kluyveromyces marxianus BioBERT-Optimized Chunks},
-  author = {Quantum Chunking System},
+  author = {Milad96},
+  title = {Kluyveromyces marxianus BioBERT Chunks},
   year = {2024},
   publisher = {HuggingFace},
   url = {https://huggingface.co/datasets/Milad96/Kluyveromyces-marxianus-chunks}
 }
 ```
 
-## 📜 License
-
-Apache 2.0 - Free for commercial and research use
-
-## 🤝 Source
-
-**Original Dataset**: [Milad96/Kluyveromyces-marxianus](https://huggingface.co/datasets/Milad96/Kluyveromyces-marxianus)
-
-**Processing**: BiOMistral-7B Quantum Chunking System
-
-## 🔗 Resources
-
-- [BioBERT](https://huggingface.co/dmis-lab/biobert-large-cased-v1.1)
-- [BiOMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B)
-- [Transformers](https://github.com/huggingface/transformers)
-
----
+## License
 
-**Generated**: 2025-11-01 08:35:58
-**System**: BiOMistral Quantum Chunking v1.0
-**Target**: BioBERT-Large Fine-tuning
+Apache 2.0