updated README

README.md

@@ -34,4 +34,105 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-sa-4.0
+task_categories:
+- question-answering
+language:
+- en
+pretty_name: RLHN-100K
+size_categories:
+- 10K<n<100K
 ---
+
+# Dataset Card for RLHN-100K
+
+## Dataset Description
+[Repository](https://github.com/castorini/rlhn) |
+[Paper](https://huggingface.co/papers/2505.16967) |
+[ArXiv](https://arxiv.org/abs/2505.16967)
+
+RLHN is a cascading LLM framework designed to accurately relabel hard negatives in existing IR/RAG training datasets, such as MS MARCO and HotpotQA.
+
+This Tevatron dataset (100K training pairs) contains the queries, positives + relabeled hard negatives, remaining hard negatives for 7 datasets in the BGE training collection. 
+
+This repository contains the training pairs that can be used to fine-tune embedding, ColBERT or multi-vector, and reranker models. 
+
+The original dataset (bad quality; containing false negatives) can be found at [rlhn/default-100K](https://huggingface.co/datasets/rlhn/default-100K/).
+
+> Note: RLHN datasets are not **new** training datasets, but rather existing BGE collection training datasets with hard negatives cleaned!
+
+## Dataset Structure
+
+To access the data using HuggingFace `datasets`:
+```python
+rlhn = datasets.load_dataset('rlhn/rlhn-100K')
+
+# training set:
+for data in freshstack['train']:
+    query_id = data["query_id"]                            # md5 hash of the query_id
+    query = data["query"]                                  # query text
+    subset = data["subset"]                                # training dataset, e.g., fiqa or msmarco_passage
+
+    # positive passages
+    for positive_passage in data["positive_passages"]:
+        doc_id = positive_passage["docid"]
+        title = positive_passage["title"]                  # title is usually empty, added in text
+        text = positive_passage["text"]                    # contains both the title & text
+
+    # hard negative passages
+    for negative_passage in data["negative_passages"]:
+        doc_id = negative_passage["docid"]
+        title = negative_passage["title"]                  # title is usually empty, added in text
+        text = negative_passage["text"]                    # contains both the title & text
+```
+
+
+## Original Dataset Statistics 
+The following table contains the number of training pairs for each training dataset included in RLHN. These numbers are for the default setting.  
+
+| Dataset           | 100K splits | 250K splits | 400K splits | 680K splits  |
+|-------------------|-------------|-------------|-------------|------------- |
+| arguana           | 4,065       | 4,065       | 4,065       | 4,065        |
+| fever             | 28,755      | 28,755      | 28,755      | 28,755       |
+| fiqa              | 5,500       | 5,500       | 5,500       | 5,500        |
+| hotpotqa          | 10,250      | 30,000      | 84,516      | 84,516       |
+| msmarco_passage   | 49,571      | 145,000     | 210,000     | 485,823      |
+| nq                | 6,110       | 30,000      | 58,568      | 58,568       |
+| scidocsrr         | 12,654      | 12,654      | 12,654      | 12,654       |
+| **total**         | **96,167**  | **255,974** | **404,058** | **679,881**  |
+
+
+## License
+The RLHN dataset is made available with the CC-BY-SA 4.0 license.
+
+## Hashing & IDs
+
+We generate the md5 hash as the unique identifier (ID) for both the query \& documents, using the code below:
+
+```python
+import hashlib
+
+def get_md5_hash(text):
+  """Calculates the MD5 hash of a given string.
+  Args:
+    text: The string to hash.
+  Returns:
+    The MD5 hash of the string as a hexadecimal string.
+  """
+  text_bytes = text.encode('utf-8')  # Encode the string to bytes
+  md5_hash = hashlib.md5(text_bytes).hexdigest()
+  return md5_hash
+```
+
+## Citation
+```
+@misc{thakur2025relabel,
+      title={Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval}, 
+      author={Nandan Thakur and Crystina Zhang and Xueguang Ma and Jimmy Lin},
+      year={2025},
+      eprint={2505.16967},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2505.16967}, 
+}
+```