add basic dataest card info

README.md

@@ -52,3 +52,106 @@ dataset_info:
   dataset_size: 460874306
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 # Dataset Card for unarXive IMRaD classification
+
+## 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 IMRaD classification dataset contains 530k paragraphs from computer science papers and the IMRaD section they originate from. The paragraphs are derived from [unarXive](https://github.com/IllDepence/unarXive).
+
+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 one of the labels ('i', 'm', 'r', 'd', 'w' — for Introduction, Methods, Results, Discussion and Related Work). An example is shown below.
+
+```
+{'_id': '789f68e7-a1cc-4072-b07d-ecffc3e7ca38',
+ 'label': 'm',
+ 'text': 'To link the mentions encoded by BERT to the KGE entities, we define '
+         'an entity linking loss as cross-entropy between self-supervised '
+         'entity labels and similarities obtained from the linker in KGE '
+         'space:\n'
+         '\\(\\mathcal {L}_{EL}=\\sum -\\log \\dfrac{\\exp (h_m^{proj}\\cdot '
+         '\\textbf {e})}{\\sum _{\\textbf {e}_j\\in \\mathcal {E}} \\exp '
+         '(h_m^{proj}\\cdot \\textbf {e}_j)}\\) \n'}
+```
+
+### Data Splits
+
+The data is split into training, development, and testing data as follows.
+
+* Training: 520,053 instances
+* Development: 5000 instances
+* Testing: 5001 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 given 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
+
+Class labels were automatically determined ([see implementation](https://github.com/IllDepence/src/utility_scripts/ml_tasks_prep_data.py).
+
+## Considerations for Using the Data
+
+### Discussion and Biases
+
+Because only paragraphs unambiguously assignable to one of the IMRaD classeswere used, a certain selection bias is to be expected in the data.
+
+### Other Known Limitations
+
+Depending on authors’ writing styles as well LaTeX processing quirks, paragraphs can vary in length a significantly.
+
+## Additional Information
+
+### Licensing information
+
+The dataset is released under the Creating Commons Attribution Share Alike license 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}
+}
+```