add information to dataset card

README.md

@@ -59,3 +59,115 @@ dataset_info:
   download_size: 7005370567
   dataset_size: 6594770814
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+# Dataset Card for unarXive citation recommendation
+
+## Dataset Description
+
+* **Homepage:** [https://github.com/IllDepence/unarXive](https://github.com/IllDepence/unarXive)
+* **Paper:** [unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network](https://arxiv.org/abs/2303.14957)
+
+### Dataset Summary
+
+The unarXive citation recommendation dataset contains 2.5 Million paragraphs from computer science papers and with an annotated citation marker. The paragraphs and citation information is derived from [unarXive](https://github.com/IllDepence/unarXive).
+
+Note that citation infromation is only given as the [OpenAlex](https://openalex.org/) ID of the cited paper. An important consideration for models is therefore if the data is used *as is*, or if additional information of the cited papers (metadata, abstracts, full-text, etc.) is used.
+
+The dataset can be used as follows.
+
+```
+from datasets import load_dataset
+
+citrec_data = load_dataset('saier/unarXive_citrec')
+citrec_data = citrec_data.class_encode_column('label')  # assign target label column
+citrec_data = citrec_data.remove_columns('_id')         # remove sample ID column
+```
+
+## Dataset Structure
+
+### Data Instances
+
+Each data instance contains the paragraph’s text as well as information on one of the contained citation markers, in the form of a label (cited document OpenAlex ID), citation marker, and citation marker offset. An example is shown below.
+
+```
+{'_id': '7c1464bb-1f0f-4b38-b1a3-85754eaf6ad1',
+ 'label': 'https://openalex.org/W3115081393',
+ 'marker': '[1]',
+ 'marker_offsets': [[316, 319]],
+ 'text': 'Data: For sentiment analysis on Hindi-English CM tweets, we used the '
+         'dataset provided by the organizers of Task 9 at SemEval-2020.\n'
+         'The training dataset consists of 14 thousand tweets.\n'
+         'Whereas, the validation dataset as well as the test dataset contain '
+         '3 thousand tweets each.\n'
+         'The details of the dataset are given in [1]}.\n'
+         'For this task, we did not use any external dataset.\n'}
+```
+
+### Data Splits
+
+The data is split into training, development, and testing data as follows.
+
+* Training: 2,043,192 instances
+* Development: 225,084 instances
+* Testing: 225,348 instances
+
+## Dataset Creation
+
+### Source Data
+
+The paragraph texts are extracted from the data set [unarXive](https://github.com/IllDepence/unarXive).
+
+#### Who are the source language producers?
+
+The paragraphs were written by the authors of the arXiv papers. In file `license_info.jsonl` author and text licensing information can be found for all samples, An example is shown below.
+
+```
+
+{'authors': 'Yusuke Sekikawa, Teppei Suzuki',
+ 'license': 'http://creativecommons.org/licenses/by/4.0/',
+ 'paper_arxiv_id': '2011.09852',
+ 'sample_ids': ['cc375518-347c-43d0-bfb2-f88564d66df8',
+                '18dc073e-a48e-488e-b34c-e5fc3cb8a4ca',
+                '0c2e89b3-d863-4bc2-9e11-8f6c48d867cb',
+                'd85e46cf-b11d-49b6-801b-089aa2dd037d',
+                '92915cea-17ab-4a98-aad2-417f6cdd53d2',
+                'e88cb422-47b7-4f69-9b0b-fbddf8140d98',
+                '4f5094a4-0e6e-46ae-a34d-e15ce0b9803c',
+                '59003494-096f-4a7c-ad65-342b74eed561',
+                '6a99b3f5-217e-4d3d-a770-693483ef8670']}
+```
+
+### Annotations
+
+Citation information in unarXive is automatically determined ([see implementation](https://github.com/IllDepence/unarXive/blob/master/src/match_references_openalex.py)).
+
+<!--
+
+## Considerations for Using the Data
+
+### Discussion and Biases
+
+TODO
+
+### Other Known Limitations
+
+TODO
+
+-->
+
+## Additional Information
+
+### Licensing information
+
+The dataset is released under the Creative Commons Attribution-ShareAlike 4.0.
+
+### Citation Information
+
+```
+@inproceedings{Saier2023unarXive,
+  author        = {Saier, Tarek and Krause, Johan and F\"{a}rber, Michael},
+  title         = {{unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network}},
+  booktitle     = {Proceedings of the 23rd ACM/IEEE Joint Conference on Digital Libraries},
+  year          = {2023},
+  series        = {JCDL '23}
+}
+```