metadata

pretty_name: TheBlueScrubs-v1 (train) — fixed schema
tags:
  - medical
  - healthcare
  - biology
  - text
  - pretraining
  - safety
  - classification
  - generation
task_categories:
  - text-generation
  - text-classification
language:
  - en
license: apache-2.0
size_categories:
  - 10B<n<100B
dataset_info:
  features:
    - name: text
      dtype: string

mkurman/TheBlueScrubs-v1-fixed

What is this?

TheBlueScrubs-v1-fixed is a maintenance fork of the upstream TheBlueScrubs/TheBlueScrubs-v1 train split that resolves a schema bug in the meta column.
In the original train files, some rows serialized meta incorrectly (appearing as the literal string "dict"). This fork re-exports the entire train split without meta column, preserving text field and values.

Document count: 11,080,331 texts (train)
Tokens (upstream estimate across all splits): ~20B tokens
Sources: Curated from SlimPajama/RedPajama (Common Crawl, C4, GitHub, Books, arXiv, Wikipedia, StackExchange)
Quality signals: per-text medical probability (0.8–1.0) + three 1–5 LLM-based scores (relevance, precision/factual detail, safety/ethics); oncology label covering ~11B tokens across the full corpus.

Upstream details: The Blue Scrubs is a large, curated medical corpus designed for clinical LLMs, filtered via a logistic-regression screen and then Llama-3.1-70B evaluation; clinician and external checks reported high concordance. An oncology classifier adds cancer labels at scale.

Why this fork?

Fix: Removes the meta column, unblocking usage with datasets streaming and dataframe backends.
Scope: Content is otherwise unchanged relative to upstream train split (same rows, fields, and values).
Goal: Provide a drop-in train split that loads cleanly in datasets without ad-hoc parsing workarounds.

Data fields (train)

Field	Type	Description
`text`	string	Raw medical text extracted from SlimPajama/RedPajama sources.

Splits

This repository publishes the train split only (11,080,331 documents). For methods, scope, and aggregate corpus statistics (including validation/test in the upstream project), see the original dataset card and paper.

How to load

from datasets import load_dataset

# streaming
ds = load_dataset("openmed-community/TheBlueScrubs-v1-fixed", split="train", streaming=True)
row = next(iter(ds))
row["text"]

# non-streaming (if you have local storage/network bandwidth)
ds = load_dataset("openmed-community/TheBlueScrubs-v1-fixed", split="train")
ds.features