datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
jeopardy-clues
Jeopardy! Clues
568,068 Jeopardy! clues with their answers, categories, dollar values, air dates, and
round information, compiled from publicly archived, community-maintained transcriptions
of aired episodes.
Loading
from datasets import load_dataset
ds = load_dataset("robworks-software/jeopardy-clues")
science = ds["train"].filter(lambda x: x["category"] == "SCIENCE")
Splits
Split
Rows
train
482,857
validation
42,605
test
42,606… See the full description on the dataset page: https://huggingface.co/datasets/robworks-software/jeopardy-clues.biomed-fr-v3-enriched-softmin-standard
biomed-fr-v3-enriched-softmin-standard
This dataset is a quality-upsampled version of rntc/biomed-fr-v3-enriched using soft-min bottleneck sampling.
Preprocessing Method
Soft-min calculation:
Formula: s = (mean(q_k^p))^(1/p) where q_k are the 4 quality scores
Parameter p = -2.0
Weight computation:
Ratio preference (5 vs 1): R = 10
Gamma exponent: γ = 1.43 (computed as log(R)/log(5))
Weight formula: w = s^γ
Floor: w = max(w, median(w) × 0.05)
Resampling:
Target size:… See the full description on the dataset page: https://huggingface.co/datasets/rntc/biomed-fr-v3-enriched-softmin-standard.database-query-logs-synthetic
Database Query Logs (synthetic)
3,995 database query-log entries spanning 10 engines - MySQL, PostgreSQL, MongoDB, SQL
Server, Oracle, MariaDB, SQLite, Cassandra, Redis, and Elasticsearch - with query text,
type, complexity, execution timing, and row-count metadata.
These queries are synthetic
The queries were programmatically generated, not captured from production systems.
They were produced by templating a set of query shapes across industry-flavored schema… See the full description on the dataset page: https://huggingface.co/datasets/robworks-software/database-query-logs-synthetic.biomed-fr-v3-enriched-softmin-leger
biomed-fr-v3-enriched-softmin-leger
This dataset is a quality-upsampled version of rntc/biomed-fr-v3-enriched using soft-min bottleneck sampling.
Preprocessing Method
Soft-min calculation:
Formula: s = (mean(q_k^p))^(1/p) where q_k are the 4 quality scores
Parameter p = -0.7
Weight computation:
Ratio preference (5 vs 1): R = 5
Gamma exponent: γ = 1.00 (computed as log(R)/log(5))
Weight formula: w = s^γ
Floor: w = max(w, median(w) × 0.05)
Resampling:
Target size: Same… See the full description on the dataset page: https://huggingface.co/datasets/rntc/biomed-fr-v3-enriched-softmin-leger.historical-training-manuals
Historical Training Manuals
1,597 US government and government-adjacent training manuals and technical publications
sourced from the Internet Archive, spanning roughly 1800-2021. Records carry
bibliographic metadata; a subset also carries extracted full text and a machine-generated
summary.
Loading
from datasets import load_dataset
ds = load_dataset("robworks-software/historical-training-manuals")
Splits
Split
Rows
train
1,277… See the full description on the dataset page: https://huggingface.co/datasets/robworks-software/historical-training-manuals.biomed-fr-v3-enriched-softmin-tres_agressif
biomed-fr-v3-enriched-softmin-tres_agressif
This dataset is a quality-upsampled version of rntc/biomed-fr-v3-enriched using soft-min bottleneck sampling.
Preprocessing Method
Soft-min calculation:
Formula: s = (mean(q_k^p))^(1/p) where q_k are the 4 quality scores
Parameter p = -4.0
Weight computation:
Ratio preference (5 vs 1): R = 20
Gamma exponent: γ = 1.86 (computed as log(R)/log(5))
Weight formula: w = s^γ
Floor: w = max(w, median(w) × 0.02)
Resampling:
Target… See the full description on the dataset page: https://huggingface.co/datasets/rntc/biomed-fr-v3-enriched-softmin-tres_agressif.biomed-fr-v3-enriched-softmin-tres_agressif_min2
biomed-fr-v3-enriched-softmin-tres_agressif_min2
This dataset is a quality-upsampled version of rntc/biomed-fr-v3-enriched using soft-min bottleneck sampling.
Preprocessing Method
Preprocessing steps:
Pre-filtering: Removed all rows with any quality score < 2.0 (i.e., removed rows with any score = 1)
Soft-min calculation:
Formula: s = (mean(q_k^p))^(1/p) where q_k are the 4 quality scores
Parameter p = -4.0
Weight computation:
Ratio preference (5 vs 1): R = 20
Gamma… See the full description on the dataset page: https://huggingface.co/datasets/rntc/biomed-fr-v3-enriched-softmin-tres_agressif_min2.ccisd-unified-master-2024
CCISD Unified School Master (2024)
School-level records for Clear Creek Independent School District (Texas), compiled from
the district's public school pages and Texas Education Agency accountability reports.
Covers 39 schools with principal names, contact details, enrollment, and accountability
ratings.
Loading
from datasets import load_dataset
ds = load_dataset("robworks-software/ccisd-unified-master-2024")
all_schools = ds["full"] # all 39 schools… See the full description on the dataset page: https://huggingface.co/datasets/robworks-software/ccisd-unified-master-2024.