Datasets:
Tasks:
Text Retrieval
Sub-tasks:
document-retrieval
Languages:
Chinese
Multilinguality:
monolingual
Source Datasets:
https://github.com/THUIR/LeCaRDv2
query-id
stringclasses 159
values | corpus-id
stringlengths 10
10
| score
float64 1
1
|
|---|---|---|
00000 | dql7LIbSPr | 1 |
00000 | m4WvPR8LuJ | 1 |
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00000 | SC2x1sOCmO | 1 |
00000 | 7kHvqx8cYI | 1 |
00000 | hZ3NbagTOm | 1 |
00000 | Z7HAk5IK2p | 1 |
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00000 | fpwFPq9bzC | 1 |
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00001 | efQrSO42uK | 1 |
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00001 | aBfvkSIlA7 | 1 |
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00001 | Hoomw6Tx3p | 1 |
00001 | IIeCbY9dM0 | 1 |
00001 | Q4Nwmgr23I | 1 |
00001 | PdFxJ834MD | 1 |
00001 | UVpgUbph8O | 1 |
00001 | ZYbB3jMFst | 1 |
00001 | bQknlr2cMp | 1 |
00001 | cthqlIctvx | 1 |
00001 | Qd0o4mVwZp | 1 |
00002 | nAvOArxYc7 | 1 |
00002 | 0bi4Pa8GXf | 1 |
00002 | 7wu0qVyjW2 | 1 |
00002 | fNbxIuocGH | 1 |
00003 | kgSC2RqbId | 1 |
00003 | irp0AUi0P2 | 1 |
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00003 | HErRQIMOpu | 1 |
00003 | UMVmI5BRh8 | 1 |
00003 | jcKWo3ziJm | 1 |
00003 | a8VUOkQOsC | 1 |
00003 | uo4967LUcp | 1 |
00003 | l386J292ei | 1 |
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00003 | SygT2qmWvr | 1 |
00004 | AjaMOfTft6 | 1 |
00004 | OHbDTsyYVw | 1 |
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00004 | gpuDSD8cOQ | 1 |
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00004 | Q7iLWceWwx | 1 |
00004 | HHpvtPIkKs | 1 |
00004 | BpAjWZUGmg | 1 |
00004 | XZMKueP5vw | 1 |
00005 | s9VVRGc6tK | 1 |
00005 | rS0xUOt7QE | 1 |
00005 | w1XQKrsKVh | 1 |
00005 | aeycHhlJEI | 1 |
00005 | oY5RfkafiB | 1 |
00005 | BNZcZxHnRv | 1 |
00005 | DZUgv9oj7W | 1 |
00005 | JZiTwihTC1 | 1 |
00005 | KR1Ajddi2S | 1 |
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LeCaRDv2
- Original link: https://github.com/THUIR/LeCaRDv2
- The task involves identifying and retrieving the case document that best matches or is most relevant to the scenario described in each of the provided queries.
- The query set contains 159 queries, each outlining a distinct situation.
- The corpus set includes 3795 candidate case documents.
Usage
import datasets
# Download the dataset
queries = datasets.load_dataset("mteb/LeCaRDv2", "queries")
documents = datasets.load_dataset("mteb/LeCaRDv2", "corpus")
pair_labels = datasets.load_dataset("mteb/LeCaRDv2", "default")
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