src/about.py

from dataclasses import dataclass
from enum import Enum


@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str


class Tasks(Enum):
    task0 = Task("anli_r1", "acc", "ANLI")
    task1 = Task("logiqa", "acc_norm", "LogiQA")


NUM_FEWSHOT = 0

TITLE = """
<div style="text-align: center; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 3rem 2rem; border-radius: 15px; margin-bottom: 2rem; box-shadow: 0 10px 30px rgba(0,0,0,0.2);">
    <h1 style="color: white; font-size: 3.5rem; margin: 0; font-weight: 800; text-shadow: 2px 2px 4px rgba(0,0,0,0.3);">
        🧬 DNA Benchmark
    </h1>
    <p style="color: rgba(255,255,255,0.95); font-size: 1.3rem; margin-top: 1rem; font-weight: 300;">
        Evaluating DNA Foundational Models Performance
    </p>
</div>
"""

INTRODUCTION_TEXT = """
<div style="background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%); padding: 1.5rem; border-radius: 10px; border-left: 5px solid #667eea; margin-bottom: 1.5rem;">
    <p style="font-size: 1.1rem; color: #2d3748; margin: 0; line-height: 1.6;">
        Compare and analyze the performance of state-of-the-art DNA foundational models across various genomics tasks including histone modification, splicing, promoter identification, enhancer detection, and SNP classification.
    </p>
</div>
"""

LLM_BENCHMARKS_TEXT = """
<div style="background: var(--background-fill-primary, #fff);">

## 📊 About This Leaderboard

This leaderboard provides comprehensive benchmarking of DNA foundational models across multiple genomics tasks:

- **🧬 Histone Modifications**: H2AFZ, H3K27ac, H3K27me3, H3K36me3, H3K4me1/2/3, H3K9ac, H3K9me3, H4K20me1
- **✂️ Splicing**: Donor sites, Acceptor sites, All splice sites
- **📍 Promoter Identification**: TATA-containing, Non-TATA, All promoters
- **🎯 Enhancer Detection**: Enhancer identification and classification
- **🔬 SNP Classification**: eQTL causality, ClinVar pathogenic variants, OMIM pathogenic variants

### 📈 Key Metrics
- **Accuracy**: Overall prediction accuracy
- **MCC**: Matthews Correlation Coefficient
- **Weighted F1**: F1-score weighted by class support

</div>
"""