Update README.md

README.md

@@ -1,9 +1,10 @@
+
 ---
 pretty_name: YCbench
 dataset_name: ycbench
 language:
   - en
-license: unknown          # Change to "mit", "cc-by-4.0", etc. if you pick one
+license: mit
 tags:
   - finance
   - startups
@@ -18,18 +19,16 @@ task_categories:
 size_categories:
   - n<1K
 configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path: yc_w26_startups.csv
-      - split: train
-        path: yc_w26_traction.csv
-      - split: train
-        path: yc_w26_pre_demo_scores.csv
-      - split: train
-        path: yc_mentions.csv
-      - split: train
-        path: yc_mentions_early.csv
+  - 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
@@ -41,32 +40,39 @@ This dataset was introduced in the paper:
 
 ## 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 (e.g., within ~90 days until Demo Day).
+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).
 
-Instead of waiting years for exits or large funding rounds, performance is measured using a **Pre-Demo Day Score** that combines traction signals and web visibility.
+Performance is measured using a **Pre-Demo Day Score** that combines traction signals and web visibility.
 
-### Key Files
+### Available Configurations
 
-- `yc_w26_startups.csv` — List of W26 startups with basic information
-- `yc_w26_traction.csv` — Traction metrics
-- `yc_w26_pre_demo_scores.csv` — Pre-demo day scores, velocity scores, hybrid scores, etc.
-- `yc_mentions.csv` — Google/web mention counts
-- `yc_mentions_early.csv` — Early-stage mention data
+| 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
-import pandas as pd
 
-# Load individual files (recommended for now)
-df_startups = pd.read_csv("hf://datasets/benstaf/ycbench/yc_w26_startups.csv")
-df_scores   = pd.read_csv("hf://datasets/benstaf/ycbench/yc_w26_pre_demo_scores.csv")
+# 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)
+- **GitHub**: [benstaf/ycbench](https://github.com/benstaf/ycbench)