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Datasets:
benstaf
/
ycbench

Tasks:
Tabular Classification
Text Classification
Modalities:
Tabular
Text
Formats:
csv
Languages:
English
Size:
< 1K
ArXiv:
Tags:
finance
startups
ycombinator
benchmarking
tabular
prediction
Libraries:
Datasets
pandas
Polars
License:
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metadata 
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" 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 

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