|
--- |
|
task_categories: |
|
- text-generation |
|
language: |
|
- en |
|
tags: |
|
- report-generation |
|
- information retrieval |
|
- retrieval |
|
- generation |
|
- legal |
|
- law |
|
size_categories: |
|
- 1M<n<10M |
|
--- |
|
|
|
# Usage |
|
The dataset is in folder according to the task and type (e.g. `generation` or `collection` for IR). |
|
|
|
You can load a specific file (say the test set of the generation task) with this command: |
|
```python |
|
from datasets import load_dataset |
|
dataset = load_dataset("jhu-clsp/CLERC", data_files={"data": f"generation/test.jsonl"})["data"] |
|
``` |
|
|
|
# Dataset Description: |
|
CLERC is a large-scale dataset of federal case law from the paper (https://arxiv.org/abs/2406.17186). It has a few subsets: |
|
1. collection: records 1.84M original federal case law, both in doc and passage forms. This can be used for doing retrieval or alternative tasks. |
|
2. generation: includes data for the RAG task as described in the paper. Also see the github (https://github.com/bohanhou14/CLERC) for more details. |
|
3. qrels and queries: qrels and queries used for the retrieval task, all following MSMARCO formats. |
|
4. triples, teva_train_triples: triples in different formats for evaluation and training (with tevatron) |
|
|
|
|
