Datasets:

Hyukkyu
/

beir-fever

pretty_name: BEIR · FEVER
license: cc-by-nc-sa-3.0
language:
  - en
multilinguality:
  - monolingual
task_categories:
  - text-retrieval
task_ids:
  - document-retrieval
tags:
  - beir
  - mteb
  - retrieval
  - fact-checking
configs:
  - config_name: corpus
    data_files:
      - split: test
        path: corpus/test-*.parquet
  - config_name: hard-negatives
    data_files:
      - split: train
        path: hard-negatives/train-*.parquet
  - config_name: judgments
    data_files:
      - split: train
        path: judgments/train-*.parquet
  - config_name: qrels
    data_files:
      - split: test
        path: qrels/test-*.parquet
      - split: train
        path: qrels/train-*.parquet
      - split: dev
        path: qrels/dev-*.parquet
  - config_name: queries
    data_files:
      - split: test
        path: queries/test-*.parquet
      - split: train
        path: queries/train-*.parquet
      - split: dev
        path: queries/dev-*.parquet
  - config_name: teacher-scores
    data_files:
      - split: train
        path: teacher-scores/train-*.parquet

FEVER — BEIR, unified schema

A normalised copy of the dataset behind the mteb task FEVER, one of the tasks of the BEIR benchmark as mteb defines it. Same queries, documents and relevance judgements as the benchmark evaluates — reshaped into one strict schema shared by every dataset in this collection.


Source	`mteb/fever` @ `bea83ef9e8fb` (the revision pinned in `mteb`)
Domain · languages	fact-checking · eng
Queries / documents / qrels (all splits)	123,142 / 5,416,568 / 156,101
Qrels per query	min 1 · mean 1.191 · max 15
Score values	1 ×7,937
Layout	`queries` · `corpus` · `qrels`, split `test`; `queries`/`qrels` also carry `train`, `dev` — one shared corpus; `hard-negatives` and `teacher-scores` for `train`
Splits	`hard-negatives`: train · `qrels`: train, dev, test · `queries`: train, dev, test · `teacher-scores`: train
Training extension	`hard-negatives` and `teacher-scores` for the `train` split are the owner's own mining and scoring (details); `queries`/`qrels` `train` are the benchmark's training data
Hard negatives	sources: `dense` · 9,889,255 rows
Teacher scores	`jinaai/jina-reranker-v3.5` · 9,989,255 rows (positives included)
Judgments	`judgments`: `typesafe/jev-1.13.0` · 2,366,979 rows
License	`cc-by-nc-sa-3.0`

Schema

config	columns	rules
`queries`	`id: string`, `text: string`	ids unique and non-empty; every query has ≥ 1 qrel
`corpus`	`id: string`, `title: string`, `text: string`	`title` is always present (`""` when the source has none)
`qrels`	`query-id: string`, `corpus-id: string`, `score: int32`	referential integrity to both tables; no duplicate pairs; no floats
`hard-negatives`	`query-id: string`, `corpus-id: string`, `rank: int32`, `source: string`	one row per mined negative; `(query-id, corpus-id, source)` unique; never a labelled positive of the same query
`teacher-scores`	`query-id: string`, `corpus-id: string`, `teacher: string`, `score: float32`	one row per scored pair (positives included); a row means scored — never a placeholder
`judgments`	`query-id: string`, `corpus-id: string`, `judge: string`, `role: string`, `p_yes: float32`, `round: int32`	one row per judged pair; `role` is `positive` (the training positive) or `candidate` (a mined candidate, never a labelled negative); `p_yes` in [0, 1]; `round` 0 the first request, 1.. top-ups

Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule above is enforced by a validator before publishing; provenance.json records the source file hashes, what changed, and the output file hashes.

What changed from the source

byte-preserved all text — no whitespace, newline, or control-character normalisation
kept 3 qrels rows whose document is absent from the corpus — a property of the source (the source's qrels reference document ids that its corpus does not contain). mteb evaluates the source as-is, and removing them would change the ideal DCG and break equivalence with the benchmark, so they stay; the referential-integrity rule below is relaxed for this dataset only
dev: dropped 116,476 queries that have no qrels in this split (mteb does the same at load time)
test: dropped 116,476 queries that have no qrels in this split (mteb does the same at load time)
train: dropped 13,332 queries that have no qrels in this split (mteb does the same at load time)
cast qrels.score double -> int32
kept 31,703 documents whose text is identical to another document's — a property of the source; note that models embed such documents identically and retrieval ties among them are broken by corpus order
renamed _id → id
renamed source splits (queries←queries/queries, corpus←corpus/corpus) to test

Hard negatives and teacher scores

Filled by the owner's annotation pipeline (annotation=jina35) for the train split of the query set(s) below; queries without a labelled positive are left out.

Candidates: dense retrieval with jinaai/jina-embeddings-v5-text-small over the full corpus to depth 1,000; 100 candidates per query drawn from the rank windows 1–30 (30), 31–100 (30), 101–300 (20), 301–1000 (20), the query's labelled positives excluded. rank is the dense rank; source is dense for a mined row and dataset for a negative the source labels itself (those are kept for every query of the split, sampled or not).
Teacher: jinaai/jina-reranker-v3.5, listwise: a query's positive and all of its candidates are scored together in one context of up to 32,768 tokens. score is the raw cosine score, one row per (query, positive) and per (query, candidate); a labelled negative that was also mined is scored once. No filtering is applied to the tables.

configs	queries	hard negatives	teacher scores
`hard-negatives` · `teacher-scores`	100,000 (seeded sample, seed 1)	9,889,255 (9,889,255 dense)	9,989,255

from datasets import load_dataset
negatives = load_dataset("Hyukkyu/beir-fever", "hard-negatives", split="train")
scores    = load_dataset("Hyukkyu/beir-fever", "teacher-scores", split="train")

Jev judgments

The judgments config holds, for every training query, whether TypeSafe's Jev (jev-1.13.0) judged its training positive and its mined candidates relevant: p_yes is Jev's P(yes) for the source's question (e.g. does the passage answer the query?). They locate the false negatives among the mined candidates and the mislabelled positives.

Requests. One request per query: its training positive, its candidates whose teacher score is at least 0.85 × the positive's (the 24 highest) and 8 random candidates below that, shuffled under neutral ids, one yes/no question per passage (round 0). Queries left with fewer than 7, then 10, candidates under the cutoff got the next hardest unjudged candidates in top-up rounds (round 1–8). The dataset's own labelled negatives were never sent. Texts were cut to 256 (query) and 512 (passage) tokens of the jina-embeddings-v5 small tokenizer. Grouped judgments run about 0.07 below single-pair ones, so the thresholds below apply to this table.
Accuracy (blind-labelled audit of 1,946 pairs): a candidate at or above its source's cutoff is relevant 73% of the time, one below it 9%; a positive under 0.15 is mislabelled 80% of the time, except in AG News, NPR and HotpotQA, whose positives are right by construction (Jev's flags there were 0–17% right).
Use (the SPARSE loader, annotation.filter.judge): a candidate at P(yes) ≥ the cutoff is never a negative; a positive under 0.15 is replaced by the candidate Jev scores highest if that is ≥ 0.8, else the query is dropped; a candidate at ≥ 0.9 can become an extra positive.

config	queries	rows	candidates per query	top-up rows	candidate cutoff	positive check
`judgments`	100,000	2,366,979	22.7	1,320,973	0.58	< 0.15

Load it

from datasets import load_dataset
queries = load_dataset("Hyukkyu/beir-fever", "queries", split="test")
corpus  = load_dataset("Hyukkyu/beir-fever", "corpus", split="test")
qrels   = load_dataset("Hyukkyu/beir-fever", "qrels", split="test")

Cross-check against the previous layout

This repo previously held the same dataset in BEIR's raw two-repo form (Hyukkyu/beir-fever + Hyukkyu/beir-fever-qrels; that layout is kept under the git tag legacy-layout). Before replacing it, the build was compared against it: corpus id set identical (5,416,568 documents); qrels rows per split test 7,937 = 7,937, train 140,085 = 140,085, dev 8,079 = 8,079.

License and attribution

The data is redistributed under the source's terms — cc-by-nc-sa-3.0. All credit belongs to the original authors; see the source repository and the references in mteb's task metadata. This repository is an independent repackaging and is not affiliated with the RTEB or MTEB maintainers.

License discrepancy. The upstream dataset card declares cc-by-nc-sa-3.0 while mteb's TaskMetadata declares None. This card carries the upstream value as the more conservative choice.