Create README.md

README.md

@@ -0,0 +1,93 @@
+---
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path:
+    - train_0.jsonl
+    - train_1.jsonl
+task_categories:
+- question-answering
+- feature-extraction
+tags:
+- InformationRetrieval
+- MSMARCO
+- IR
+size_categories:
+- 100K<n<1M
+---
+
+
+The MSMARCO training data used to train the models from [Hypencoder: Hypernetworks for Information Retrieval
+](https://arxiv.org/abs/2502.05364). 
+
+# Dataset Overview
+This dataset is based on the MSMARCO Passage dataset and includes all the queries which have a positive passage in the original dataset (there are additional queries with no positive passages which we do not use). Each query has the known positive passage as well as 200 additional passages. These additional passages may be unlabeled positives or negatives. Each associated passage has a score from [cross-encoder/ms-marco-MiniLM-L-12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) which were used for distillation training. The passages are ordered by the teacher score.
+
+# Dataset Construction
+The 200 associated passages for each query were found by retrieving the top 800 passages using an early version of Hypencoder and then (1) selecting the top 100 passages (2) another 100 from the bottom 700 passages uniformly at random. Additionally, the known relevant passage provided by MSMARCO is added.
+
+All query-passage pairs were then scored with [cross-encoder/ms-marco-MiniLM-L-12-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L-6-v2) to get teacher scores.
+
+# Dataset Structure
+```
+{
+  "query": {
+    "id": query ID which is the same as the one used in MSMARCO,
+    "content": query text,
+    "tokenized_content": token IDs for bert-base-uncased,
+  },
+  "items": [
+    {
+      "id": passage ID which is the same as the one used in MSMARCO,
+      "content": passage text,
+      "tokened_content": token IDs for bert-base-uncased,
+      "score": Teacher score from 'cross-encoder/ms-marco-MiniLM-L-12-v2/',
+      "type": Has two values [null, "given"] if the passage was the provided positive by MSMARCO the type is "given" otherwise it is null.
+    },
+  ]
+}
+```
+
+# Cite
+If you use this dataset please cite our paper:
+```
+@misc{killingback2025hypencoderhypernetworksinformationretrieval,
+      title={Hypencoder: Hypernetworks for Information Retrieval}, 
+      author={Julian Killingback and Hansi Zeng and Hamed Zamani},
+      year={2025},
+      eprint={2502.05364},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2502.05364}, 
+}
+```
+and the original MSMARCO paper:
+```
+@inproceedings{MSMARCO,
+  author       = {Tri Nguyen and
+                  Mir Rosenberg and
+                  Xia Song and
+                  Jianfeng Gao and
+                  Saurabh Tiwary and
+                  Rangan Majumder and
+                  Li Deng},
+  editor       = {Tarek Richard Besold and
+                  Antoine Bordes and
+                  Artur S. d'Avila Garcez and
+                  Greg Wayne},
+  title        = {{MS} {MARCO:} {A} Human Generated MAchine Reading COmprehension Dataset},
+  booktitle    = {Proceedings of the Workshop on Cognitive Computation: Integrating
+                  neural and symbolic approaches 2016 co-located with the 30th Annual
+                  Conference on Neural Information Processing Systems {(NIPS} 2016),
+                  Barcelona, Spain, December 9, 2016},
+  series       = {{CEUR} Workshop Proceedings},
+  volume       = {1773},
+  publisher    = {CEUR-WS.org},
+  year         = {2016},
+  url          = {https://ceur-ws.org/Vol-1773/CoCoNIPS\_2016\_paper9.pdf},
+  timestamp    = {Thu, 11 Apr 2024 13:33:56 +0200},
+  biburl       = {https://dblp.org/rec/conf/nips/NguyenRSGTMD16.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+```