Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Languages:
English
Size:
10K - 100K
query-id
stringlengths 8
9
| corpus-id
stringlengths 10
11
| score
float64 1
1
|
|---|---|---|
1000-2_q1 | 1000-2_doc1 | 1 |
1000-2_q2 | 1000-2_doc2 | 1 |
1000-3_q1 | 1000-3_doc1 | 1 |
1000-3_q2 | 1000-3_doc2 | 1 |
1001-2_q1 | 1001-2_doc1 | 1 |
1001-2_q2 | 1001-2_doc2 | 1 |
1001-3_q1 | 1001-3_doc1 | 1 |
1001-3_q2 | 1001-3_doc2 | 1 |
1002-2_q1 | 1002-2_doc1 | 1 |
1002-2_q2 | 1002-2_doc2 | 1 |
1002-3_q1 | 1002-3_doc1 | 1 |
1002-3_q2 | 1002-3_doc2 | 1 |
1003-2_q1 | 1003-2_doc1 | 1 |
1003-2_q2 | 1003-2_doc2 | 1 |
1003-3_q1 | 1003-3_doc1 | 1 |
1003-3_q2 | 1003-3_doc2 | 1 |
1004-2_q1 | 1004-2_doc1 | 1 |
1004-2_q2 | 1004-2_doc2 | 1 |
1004-3_q1 | 1004-3_doc1 | 1 |
1004-3_q2 | 1004-3_doc2 | 1 |
1005-2_q1 | 1005-2_doc1 | 1 |
1005-2_q2 | 1005-2_doc2 | 1 |
1005-3_q1 | 1005-3_doc1 | 1 |
1005-3_q2 | 1005-3_doc2 | 1 |
1006-2_q1 | 1006-2_doc1 | 1 |
1006-2_q2 | 1006-2_doc2 | 1 |
1006-3_q1 | 1006-3_doc1 | 1 |
1006-3_q2 | 1006-3_doc2 | 1 |
1007-2_q1 | 1007-2_doc1 | 1 |
1007-2_q2 | 1007-2_doc2 | 1 |
1007-3_q1 | 1007-3_doc1 | 1 |
1007-3_q2 | 1007-3_doc2 | 1 |
1008-2_q1 | 1008-2_doc1 | 1 |
1008-2_q2 | 1008-2_doc2 | 1 |
1008-3_q1 | 1008-3_doc1 | 1 |
1008-3_q2 | 1008-3_doc2 | 1 |
1009-2_q1 | 1009-2_doc1 | 1 |
1009-2_q2 | 1009-2_doc2 | 1 |
1009-3_q1 | 1009-3_doc1 | 1 |
1009-3_q2 | 1009-3_doc2 | 1 |
1010-2_q1 | 1010-2_doc1 | 1 |
1010-2_q2 | 1010-2_doc2 | 1 |
1010-3_q1 | 1010-3_doc1 | 1 |
1010-3_q2 | 1010-3_doc2 | 1 |
1011-3_q1 | 1011-3_doc1 | 1 |
1011-3_q2 | 1011-3_doc2 | 1 |
1012-2_q1 | 1012-2_doc1 | 1 |
1012-2_q2 | 1012-2_doc2 | 1 |
1012-3_q1 | 1012-3_doc1 | 1 |
1012-3_q2 | 1012-3_doc2 | 1 |
1013-2_q1 | 1013-2_doc1 | 1 |
1013-2_q2 | 1013-2_doc2 | 1 |
1013-3_q1 | 1013-3_doc1 | 1 |
1013-3_q2 | 1013-3_doc2 | 1 |
1014-2_q1 | 1014-2_doc1 | 1 |
1014-2_q2 | 1014-2_doc2 | 1 |
1014-3_q1 | 1014-3_doc1 | 1 |
1014-3_q2 | 1014-3_doc2 | 1 |
1015-2_q1 | 1015-2_doc1 | 1 |
1015-2_q2 | 1015-2_doc2 | 1 |
1015-3_q1 | 1015-3_doc1 | 1 |
1015-3_q2 | 1015-3_doc2 | 1 |
1016-2_q1 | 1016-2_doc1 | 1 |
1016-2_q2 | 1016-2_doc2 | 1 |
1016-3_q1 | 1016-3_doc1 | 1 |
1016-3_q2 | 1016-3_doc2 | 1 |
1017-2_q1 | 1017-2_doc1 | 1 |
1017-2_q2 | 1017-2_doc2 | 1 |
1017-3_q1 | 1017-3_doc1 | 1 |
1017-3_q2 | 1017-3_doc2 | 1 |
1018-2_q1 | 1018-2_doc1 | 1 |
1018-2_q2 | 1018-2_doc2 | 1 |
1018-3_q1 | 1018-3_doc1 | 1 |
1018-3_q2 | 1018-3_doc2 | 1 |
1019-2_q1 | 1019-2_doc1 | 1 |
1019-2_q2 | 1019-2_doc2 | 1 |
1019-3_q1 | 1019-3_doc1 | 1 |
1019-3_q2 | 1019-3_doc2 | 1 |
1020-3_q1 | 1020-3_doc1 | 1 |
1020-3_q2 | 1020-3_doc2 | 1 |
1021-2_q1 | 1021-2_doc1 | 1 |
1021-2_q2 | 1021-2_doc2 | 1 |
1021-3_q1 | 1021-3_doc1 | 1 |
1021-3_q2 | 1021-3_doc2 | 1 |
1022-2_q1 | 1022-2_doc1 | 1 |
1022-2_q2 | 1022-2_doc2 | 1 |
1022-3_q1 | 1022-3_doc1 | 1 |
1022-3_q2 | 1022-3_doc2 | 1 |
1023-2_q1 | 1023-2_doc1 | 1 |
1023-2_q2 | 1023-2_doc2 | 1 |
1023-3_q1 | 1023-3_doc1 | 1 |
1023-3_q2 | 1023-3_doc2 | 1 |
1025-2_q1 | 1025-2_doc1 | 1 |
1025-2_q2 | 1025-2_doc2 | 1 |
1025-3_q1 | 1025-3_doc1 | 1 |
1025-3_q2 | 1025-3_doc2 | 1 |
1026-2_q1 | 1026-2_doc1 | 1 |
1026-2_q2 | 1026-2_doc2 | 1 |
1026-3_q1 | 1026-3_doc1 | 1 |
1026-3_q2 | 1026-3_doc2 | 1 |
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NevIR-mteb Dataset
This is the MTEB-compatible version of the NevIR dataset, structured for information retrieval tasks focused on negation understanding.
Dataset Structure
The dataset is organized into multiple configurations:
corpus: Contains all documents (doc1 and doc2 from each sample)queries: Contains all queries (q1 and q2 from each sample)qrels: Contains relevance judgments (q1 matches with doc1, q2 matches with doc2)top_ranked: Contains candidate documents for each query (both doc1 and doc2 for every query)
Usage
from datasets import load_dataset
# Load the entire dataset
dataset = load_dataset("orionweller/NevIR-mteb")
# Load specific configurations
corpus = load_dataset("orionweller/NevIR-mteb", "corpus")
queries = load_dataset("orionweller/NevIR-mteb", "queries")
qrels = load_dataset("orionweller/NevIR-mteb", "qrels")
top_ranked = load_dataset("orionweller/NevIR-mteb", "top_ranked")
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