Update README.md

README.md

@@ -1,24 +1,69 @@
----
-license: apache-2.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: id
-    dtype: string
-  - name: contents
-    dtype: string
-  - name: title
-    dtype: string
-  - name: wikipedia_id
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 18038881943
-    num_examples: 35678076
-  download_size: 10150820540
-  dataset_size: 18038881943
----
+---
+license: apache-2.0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: contents
+    dtype: string
+  - name: title
+    dtype: string
+  - name: wikipedia_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 18038881943
+    num_examples: 35678076
+  download_size: 10150820540
+  dataset_size: 18038881943
+language:
+- en
+---
+
+# KILT Corpus
+
+This dataset contains approximately 36 million Wikipedia passages from the "[Multi-task retrieval for knowledge-intensive tasks](https://arxiv.org/pdf/2101.00117)" paper. It is also the retrieval corpus used in the paper [Chain-of-Retrieval Augmented Generation](https://arxiv.org/pdf/2501.14342).
+
+## Fields
+
+* `id`: A unique identifier for each passage.
+* `title`: The title of the Wikipedia page from which the passage originates.
+* `contents`: The textual content of the passage.
+* `wikipedia_id`: The unique identifier for the Wikipedia page, used for KILT evaluation.
+
+## How to Load the Dataset
+
+You can easily load this dataset using the `datasets` library from Hugging Face. Make sure you have the library installed (`pip install datasets`).
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset('corag/kilt-corpus', split='train')
+
+# You can inspect the dataset structure and the first few examples:
+print(ds)
+print(ds[0])
+```
+
+## References
+
+```
+@article{maillard2021multi,
+  title={Multi-task retrieval for knowledge-intensive tasks},
+  author={Maillard, Jean and Karpukhin, Vladimir and Petroni, Fabio and Yih, Wen-tau and O{\u{g}}uz, Barlas and Stoyanov, Veselin and Ghosh, Gargi},
+  journal={arXiv preprint arXiv:2101.00117},
+  year={2021}
+}
+
+@article{wang2025chain,
+  title={Chain-of-Retrieval Augmented Generation},
+  author={Wang, Liang and Chen, Haonan and Yang, Nan and Huang, Xiaolong and Dou, Zhicheng and Wei, Furu},
+  journal={arXiv preprint arXiv:2501.14342},
+  year={2025}
+}
+```