datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
TREC-QC
TREC Question Classification
Question classification in coarse and fine-grained categories.
Source:
Experimental Data for Question Classification
Xin Li, Dan Roth, Learning Question Classifiers. COLING'02, Aug., 2002.
trec-covid-qrels
Dataset Card for BEIR Benchmark
Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
Fact-checking: FEVER, Climate-FEVER, SciFact
Question-Answering: NQ, HotpotQA, FiQA-2018
Bio-Medical IR: TREC-COVID, BioASQ, NFCorpus
News Retrieval: TREC-NEWS, Robust04
Argument Retrieval: Touche-2020, ArguAna
Duplicate Question Retrieval: Quora, CqaDupstack
Citation-Prediction: SCIDOCS
Tweet… See the full description on the dataset page: https://huggingface.co/datasets/BeIR/trec-covid-qrels.trec-covid-decontaminated
trec-covid (Decontaminated)
A decontaminated version of the trec-covid dataset from the BEIR benchmark, with samples found in the mgte-en pre-training dataset removed.
Decontamination methodology
Contamination was detected using a two-pass approach against the full mgte-en dataset (484 GB, 1,235 parquet files):
Pass 1: Exact hash matching
All texts (queries and corpus documents) were normalized (lowercased, unicode NFKD, whitespace collapsed) and hashed with… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/trec-covid-decontaminated.trec-ragtime-2026
TREC RAGTIME 2026 — sentence and passage renderings
A sentence-level view of the TREC RAGTIME 2026 news collection,
with two English machine translations of every non-English sentence and the passage boundaries used
for retrieval. Derived from trec-ragtime/ragtime2.
Pipeline, experiment design, run configurations and reproduction steps:
github.com/jknafou/trec-ragtime-2026
What is in here
Config
Splits
Rows
Contents
sentences
eng, spa, rus, zho
88,719… See the full description on the dataset page: https://huggingface.co/datasets/jknafou/trec-ragtime-2026.trec6trecqa
Dataset Card for "trecqa"
TREC-QA dataset for Answer Sentence Selection. The dataset contains 2 additional splits which are clean versions of the original development and test sets. clean versions contain only questions which have at least a positive and a negative answer candidate.
clinical-trials-trec-qrelsclinical-trials-trec-topicstrec_covid_toyset_pairtrec-covid-decontaminated
trec-covid-decontaminated (MTEB layout)
Repackaging of lightonai/trec-covid-decontaminated
into the layout expected by MTEB:
a default config holding the qrels (one split per evaluation split), alongside
corpus and queries configs.
Rows and columns are copied verbatim from the source dataset; only the
config/split packaging differs. All credit for the decontaminated data belongs to
LightOn AI, and to the original BEIR
authors for the underlying benchmark.
Source:… See the full description on the dataset page: https://huggingface.co/datasets/iamfortytwo/trec-covid-decontaminated.trec6_Full-p_1trec-bert-scaledtrec6_Full-p_05trec-covid-pl-qrelsPart of BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: konrad.wojtasik@pwr.edu.pl
rus-trec-covid-qrelsUDR_TREC
Dataset Card for "UDR_TREC"
More Information needed
trec6_Sampled-p_1trec6_Sampled-p_05trec_covid_toysetHELMET_icl_trec_fine_6400shot_balance__evaltrec-bertHELMET_icl_trec_coarse_6600shot_balance__evaltrec-ja
日本語TRECライクな質問分類データセット
概要
trec-ja.json
このデータセットは、日本語の質問文とその分類ラベルを含む、TRECデータセットを模した質問分類用のデータセットです。日本の文化や地理に関連する質問を含み、自然言語処理や機械学習のタスクに適しています。
データセットの特徴
質問数: 535
言語: 日本語
粗粒度ラベル数: 6
細粒度ラベル数: 50
データ構造
各データポイントは以下の構造を持っています：
{
"text": "質問文",
"coarse_label": 粗粒度ラベル（整数）,
"fine_label": 細粒度ラベル（整数）
}
ラベルの説明
粗粒度ラベル
0: 略語 (ABBR)
1: エンティティ (ENTY)
2: 説明 (DESC)
3: 人物 (HUM)
4: 場所 (LOC)
5: 数値 (NUM)
細粒度ラベル… See the full description on the dataset page: https://huggingface.co/datasets/deltacore/trec-ja.