Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Size:
< 1K
Tags:
text-retrieval
File size: 1,559 Bytes
52ab2d8 3771d71 52ab2d8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
---
task_categories:
- text-retrieval
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
- config_name: corpus
features:
- name: id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
- config_name: queries
features:
- name: id
dtype: string
- name: text
dtype: string
configs:
- config_name: default
data_files:
- split: test
path: relevance.jsonl
- config_name: corpus
data_files:
- split: corpus
path: corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: queries.jsonl
---
This dataset comprises approximately 3,000 Supreme Court of India case documents and is designed to evaluae the retrieval of relevant prior cases for given legal situations. It includes 50 queries, each outlining a specific scenario. We include this dataset in the benchmark because the documents are reasonably challenging, the queries are non-synthetic, and the labels are of high quality.
**Usage**
```
import datasets
# Download the dataset
queries = datasets.load_dataset("embedding-benchmark/AILACasedocs", "queries")
documents = datasets.load_dataset("embedding-benchmark/AILACasedocs", "corpus")
pair_labels = datasets.load_dataset("embedding-benchmark/AILACasedocs", "default")
``` |