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---
pretty_name: YCbench
dataset_name: ycbench
language:
- en
license: mit
tags:
- finance
- startups
- ycombinator
- benchmarking
- tabular
- prediction
- venture-capital
task_categories:
- tabular-classification
- text-classification
size_categories:
- n<1K
configs:
- config_name: startups
data_files: yc_w26_startups.csv
- config_name: traction
data_files: yc_w26_traction.csv
- config_name: scores
data_files: yc_w26_pre_demo_scores.csv
- config_name: mentions
data_files: yc_mentions.csv
- config_name: mentions_early
data_files: yc_mentions_early.csv
---
# YCbench
**A live benchmark dataset for forecasting startup outperformance in Y Combinator batches.**
This dataset was introduced in the paper:
**["YC Bench: a Live Benchmark for Forecasting Startup Outperformance in Y Combinator Batches"](https://huggingface.co/papers/2604.02378)** by Mostapha Benhenda.
## Dataset Overview
YCbench provides structured public signals for Y Combinator startups (focused on the **W26** batch of 196 companies). It enables rapid evaluation of models that predict which startups will **outperform their batch peers** in the short term (until Demo Day).
Performance is measured using a **Pre-Demo Day Score** that combines traction signals and web visibility.
### Available Configurations
| Config | File | Description |
|-----------------|-------------------------------|------------------------------------------|
| `startups` | `yc_w26_startups.csv` | Basic startup information |
| `traction` | `yc_w26_traction.csv` | Traction metrics |
| `scores` | `yc_w26_pre_demo_scores.csv` | Pre-demo day scores + velocity |
| `mentions` | `yc_mentions.csv` | Google/web mention counts |
| `mentions_early`| `yc_mentions_early.csv` | Early-stage mention data |
## Quick Load
```python
from datasets import load_dataset
# Load specific parts
startups = load_dataset("benstaf/ycbench", "startups")
scores = load_dataset("benstaf/ycbench", "scores")
mentions = load_dataset("benstaf/ycbench", "mentions")
```
Or with pandas (simple way):
```
import pandas as pd
df = pd.read_csv("hf://datasets/benstaf/ycbench/yc_w26_pre_demo_scores.csv")
```
## Links
- **Paper**: [arXiv 2604.02378](https://huggingface.co/papers/2604.02378)
- **Live Benchmark**: [ycbench.com](https://ycbench.com/)
- **GitHub**: [benstaf/ycbench](https://github.com/benstaf/ycbench)
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