README.md

---
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")
```