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Cannot get the split names for the dataset.
Error code:   SplitsNamesError
Exception:    ValueError
Message:      BuilderConfig must have a name, got 
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/responses/", line 79, in get_splits_response
                  split_full_names = get_dataset_split_full_names(dataset, hf_token)
                File "/src/services/worker/src/worker/responses/", line 39, in get_dataset_split_full_names
                  return [
                File "/src/services/worker/src/worker/responses/", line 42, in <listcomp>
                  for split in get_dataset_split_names(dataset, config, use_auth_token=hf_token)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 426, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 355, in get_dataset_config_info
                  builder = load_dataset_builder(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 1478, in load_dataset_builder
                  builder_instance: DatasetBuilder = builder_cls(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 1285, in __init__
                  super().__init__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 303, in __init__
                  self.config, self.config_id = self._create_builder_config(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 464, in _create_builder_config
                  raise ValueError(f"BuilderConfig must have a name, got {}")
              ValueError: BuilderConfig must have a name, got

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YAML Metadata Error: "licenses" is deprecated. Use "license" instead.
YAML Metadata Error: "languages" is deprecated. Use "language" instead.

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:

All these datasets have been preprocessed and can be used for your experiments.

Supported Tasks and Leaderboards

The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.

The current best performing models can be found here.


All tasks are in English (en).

Dataset Structure

All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:

  • corpus file: a .jsonl file (jsonlines) that contains a list of dictionaries, each with three fields _id with unique document identifier, title with document title (optional) and text with document paragraph or passage. For example: {"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}
  • queries file: a .jsonl file (jsonlines) that contains a list of dictionaries, each with two fields _id with unique query identifier and text with query text. For example: {"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}
  • qrels file: a .tsv file (tab-seperated) that contains three columns, i.e. the query-id, corpus-id and score in this order. Keep 1st row as header. For example: q1 doc1 1

Data Instances

A high level example of any beir dataset:

corpus = {
    "doc1" : {
        "title": "Albert Einstein", 
        "text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
                 one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
                 its influence on the philosophy of science. He is best known to the general public for his mass–energy \
                 equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
                 Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
                 of the photoelectric effect', a pivotal step in the development of quantum theory."
    "doc2" : {
        "title": "", # Keep title an empty string if not present
        "text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
                 malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
                 with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."

queries = {
    "q1" : "Who developed the mass-energy equivalence formula?",
    "q2" : "Which beer is brewed with a large proportion of wheat?"

qrels = {
    "q1" : {"doc1": 1},
    "q2" : {"doc2": 1},

Data Fields

Examples from all configurations have the following features:


  • corpus: a dict feature representing the document title and passage text, made up of:
    • _id: a string feature representing the unique document id
      • title: a string feature, denoting the title of the document.
      • text: a string feature, denoting the text of the document.


  • queries: a dict feature representing the query, made up of:
    • _id: a string feature representing the unique query id
    • text: a string feature, denoting the text of the query.


  • qrels: a dict feature representing the query document relevance judgements, made up of:
    • _id: a string feature representing the query id
      • _id: a string feature, denoting the document id.
      • score: a int32 feature, denoting the relevance judgement between query and document.

Data Splits

Dataset Website BEIR-Name Type Queries Corpus Rel D/Q Down-load md5
MSMARCO Homepage msmarco train
6,980 8.84M 1.1 Link 444067daf65d982533ea17ebd59501e4
TREC-COVID Homepage trec-covid test 50 171K 493.5 Link ce62140cb23feb9becf6270d0d1fe6d1
NFCorpus Homepage nfcorpus train
323 3.6K 38.2 Link a89dba18a62ef92f7d323ec890a0d38d
BioASQ Homepage bioasq train
500 14.91M 8.05 No How to Reproduce?
NQ Homepage nq train
3,452 2.68M 1.2 Link d4d3d2e48787a744b6f6e691ff534307
HotpotQA Homepage hotpotqa train
7,405 5.23M 2.0 Link f412724f78b0d91183a0e86805e16114
FiQA-2018 Homepage fiqa train
648 57K 2.6 Link 17918ed23cd04fb15047f73e6c3bd9d9
Signal-1M(RT) Homepage signal1m test 97 2.86M 19.6 No How to Reproduce?
TREC-NEWS Homepage trec-news test 57 595K 19.6 No How to Reproduce?
ArguAna Homepage arguana test 1,406 8.67K 1.0 Link 8ad3e3c2a5867cdced806d6503f29b99
Touche-2020 Homepage webis-touche2020 test 49 382K 19.0 Link 46f650ba5a527fc69e0a6521c5a23563
CQADupstack Homepage cqadupstack test 13,145 457K 1.4 Link 4e41456d7df8ee7760a7f866133bda78
Quora Homepage quora dev
10,000 523K 1.6 Link 18fb154900ba42a600f84b839c173167
DBPedia Homepage dbpedia-entity dev
400 4.63M 38.2 Link c2a39eb420a3164af735795df012ac2c
SCIDOCS Homepage scidocs test 1,000 25K 4.9 Link 38121350fc3a4d2f48850f6aff52e4a9
FEVER Homepage fever train
6,666 5.42M 1.2 Link 5a818580227bfb4b35bb6fa46d9b6c03
Climate-FEVER Homepage climate-fever test 1,535 5.42M 3.0 Link 8b66f0a9126c521bae2bde127b4dc99d
SciFact Homepage scifact train
300 5K 1.1 Link 5f7d1de60b170fc8027bb7898e2efca1
Robust04 Homepage robust04 test 249 528K 69.9 No How to Reproduce?

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]


Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information

Cite as:

title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},


Thanks to @Nthakur20 for adding this dataset.