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README.md

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----
-annotations_creators: []
-language_creators: []
-language:
-- en
-license:
-- cc-by-sa-4.0
-multilinguality:
-- monolingual
-paperswithcode_id: beir
-pretty_name: BEIR Benchmark
-size_categories:
-  msmarco:
-  - 1M<n<10M
-  trec-covid:
-  - 100k<n<1M
-  nfcorpus:
-  - 1K<n<10K
-  nq:
-  - 1M<n<10M
-  hotpotqa:
-  - 1M<n<10M
-  fiqa:
-  - 10K<n<100K
-  arguana:
-  - 1K<n<10K
-  touche-2020:
-  - 100K<n<1M
-  cqadupstack:
-  - 100K<n<1M
-  quora:
-  - 100K<n<1M
-  dbpedia:
-  - 1M<n<10M
-  scidocs:
-  - 10K<n<100K
-  fever:
-  - 1M<n<10M
-  climate-fever:
-  - 1M<n<10M
-  scifact:
-  - 1K<n<10K
-source_datasets: []
-task_categories:
-- text-retrieval
-- zero-shot-retrieval
-- information-retrieval
-- zero-shot-information-retrieval
-task_ids:
-- passage-retrieval
-- entity-linking-retrieval
-- fact-checking-retrieval
-- tweet-retrieval
-- citation-prediction-retrieval
-- duplication-question-retrieval
-- argument-retrieval
-- news-retrieval
-- biomedical-information-retrieval
-- question-answering-retrieval
----
-
-# Dataset Card for BEIR Benchmark
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** https://github.com/UKPLab/beir
-- **Repository:** https://github.com/UKPLab/beir
-- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
-- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
-- **Point of Contact:** nandan.thakur@uwaterloo.ca
-
-### Dataset Summary
-
-BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
-
-- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
-- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
-- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
-- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
-- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
-- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
-- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
-- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
-- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
-
-All these datasets have been preprocessed and can be used for your experiments.
-
-
-```python
-
-```
-
-### 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](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
-
-### Languages
-
-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:
-
-```python
-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
-- `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
-- `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
-- `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](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``|  6,980   |  8.84M     |    1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
-| TREC-COVID |  [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50|  171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
-| NFCorpus   | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``|  323     |  3.6K     |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
-| BioASQ     | [Homepage](http://bioasq.org) | ``bioasq``|  ``train``<br>``test`` | 500    |  14.91M    |  8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
-| NQ         | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452   |  2.68M  |  1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
-| HotpotQA   | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``|  7,405   |  5.23M  |  2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip)  | ``f412724f78b0d91183a0e86805e16114`` |
-| FiQA-2018  | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``|  648     |  57K    |  2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip)  | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
-| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97   |  2.86M  |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
-| TREC-NEWS  | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news``    | ``test``| 57    |  595K    |  19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
-| ArguAna    | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406     |  8.67K    |  1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip)  | ``8ad3e3c2a5867cdced806d6503f29b99`` |
-| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49     |  382K    |  19.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
-| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 |  457K  |  1.4 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
-| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000     |  523K    |  1.6 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
-| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400    |  4.63M    |  38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
-| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000     |  25K    |  4.9 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
-| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``|  6,666     |  5.42M    |  1.2|  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip)  | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
-| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``|  1,535     |  5.42M |  3.0 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip)  | ``8b66f0a9126c521bae2bde127b4dc99d`` |
-| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``|  300     |  5K    |  1.1 |  [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip)  | ``5f7d1de60b170fc8027bb7898e2efca1`` |
-| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249  |  528K  |  69.9 |  No  |  [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04)  |
-
-
-## 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]
-
-### Annotations
-
-#### 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:
-```
-@inproceedings{
-thakur2021beir,
-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)},
-year={2021},
-url={https://openreview.net/forum?id=wCu6T5xFjeJ}
-}
-```
-
-### Contributions
-
-Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset.

arguana.py

@@ -1,58 +0,0 @@
-import json
-import csv
-import os
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_DESCRIPTION = "FIQA Dataset"
-_SPLITS = ["corpus", "queries"]
-
-URL = ""
-_URLs = {subset: URL + f"{subset}.jsonl.gz" for subset in _SPLITS}
-
-class BEIR(datasets.GeneratorBasedBuilder):
-    """BEIR BenchmarkDataset."""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name=name,
-            description=f"This is the {name} in the FiQA dataset.",
-        ) for name in _SPLITS
-    ]
-    
-    def _info(self):
-
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-             features=datasets.Features({
-                "_id": datasets.Value("string"), 
-                "title": datasets.Value("string"),
-                "text": datasets.Value("string"),
-             }),
-            supervised_keys=None,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-
-        my_urls = _URLs[self.config.name]
-        data_dir = dl_manager.download_and_extract(my_urls)
-
-        return [
-            datasets.SplitGenerator(
-                name=self.config.name,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": data_dir},
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            texts = f.readlines()
-        for i, text in enumerate(texts):
-            text = json.loads(text)
-            if 'metadata' in text: del text['metadata']
-            if "title" not in text: text["title"] = ""
-            yield i, text

corpus.jsonl.gz → corpus/arguana-corpus.parquet

@@ -1,3 +1,3 @@
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-size 3146106
+oid sha256:396fe636574694a1039601e5358efebffb47972e743ffc495e3e1c50a2ae8cfe
+size 5090536

queries.jsonl.gz → queries/arguana-queries.parquet

@@ -1,3 +1,3 @@
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-oid sha256:faae9bb245700b764c29d98a1972d1b10ebb8dfb4e7bb349741e8d4f20d29b13
-size 617785
+oid sha256:333ff2b726ebbccdd029e739bc0c85d2bc53c5906ffe6e2db701c52c590940b9
+size 947083