Update task category to text-ranking and add link to paper

README.md

@@ -1,4 +1,12 @@
 ---
+language:
+- en
+license: cc-by-sa-4.0
+size_categories:
+- 100K<n<1M
+task_categories:
+- text-ranking
+pretty_name: Remove 680K
 dataset_info:
   features:
   - name: query_id
@@ -25,34 +33,106 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 43144012642.700035
-    num_examples: 680223
-  download_size: 6653360727
-  dataset_size: 43144012642.700035
+    num_bytes: 6242007530
+    num_examples: 323622
+  download_size: 3696876038
+  dataset_size: 6242007530
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-*
-task_categories:
-- sentence-similarity
-language:
-- en
-pretty_name: BGE Retrieval Dataset (7 Datasets)
-size_categories:
-- 100K<n<1M
 ---
 
-## BGE-Retrieval-Data (7 Datasets)
-This filtered version of `nthakur/bge-full-data` includes only 7 datasets of ~678K training pairs. 
-The distribution of each subset is shown below:
-
-| Dataset | Training Pairs | Pos | Hard Negs |
-| -------: | -----------: | -----: | ------: |
-| msmarco_passage | 485823 | 1.1 | 25.0 |
-| hotpotqa | 84516 | 2.0 | 20.0 |
-| nq | 58568 | 1.0 | 98.5 |
-| fever | 29096 | 1.3 | 20.0 |
-| scidocsrr | 12655 | 1.6 | 19.7 |
-| fiqa | 5500 | 2.6 | 15.0 |
-| arguana | 4065 | 1.0 | 13.6 |
+# Dataset Card for Remove 680K
+
+## 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 (680K training pairs) contains the original queries, positives and hard negatives after dropping each training pair with a single false negative. 
+
+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-680K](https://huggingface.co/datasets/rlhn/default-680K/).
+
+> 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/remove-680K')
+
+# 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}, 
+}
+```