README.md

metadata

language:
  - zh
multilinguality:
  - monolingual
task_categories:
  - text-retrieval
source_datasets:
  - https://github.com/THUIR/LeCaRDv2
task_ids:
  - document-retrieval
config_names:
  - corpus
tags:
  - text-retrieval
dataset_info:
  - config_name: default
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: score
        dtype: float64
    splits:
      - name: test
        num_examples: 3896
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: title
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: corpus
        num_examples: 3795
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: queries
        num_examples: 159
configs:
  - config_name: default
    data_files:
      - split: test
        path: qrels/test.jsonl
  - config_name: corpus
    data_files:
      - split: corpus
        path: corpus.jsonl
  - config_name: queries
    data_files:
      - split: queries
        path: queries.jsonl

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")