Create README.md

README.md

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+---
+language:
+- en
+- fr
+- de
+- es
+- tr
+configs:
+- config_name: en
+  data_files:
+    - split: train
+      path: "RELX_en.json"
+- config_name: fr
+  data_files:
+    - split: train
+      path: "RELX_fr.json"
+- config_name: de
+  data_files:
+    - split: train
+      path: "RELX_de.json"
+- config_name: es
+  data_files:
+    - split: train
+      path: "RELX_es.json"
+- config_name: tr
+  data_files:
+    - split: train
+      path: "RELX_tr.json"
+---
+
+> [!NOTE]
+> Dataset origin: https://github.com/boun-tabi/RELX
+
+
+## RELX-Distant
+
+This dataset is gathered from Wikipedia and Wikidata.
+The process is as follows:
+
+1. The Wikipedia dumps for the corresponding languages are downloaded and converted into raw documents with Wikipedia hyperlinks in entities.
+2. The raw documents are split into sentences with spaCy (Honnibal and Montani, 2017), and all hyperlinks are converted to their corresponding Wikidata IDs.
+3. Sentences that include entity pairs with Wikidata relations (Vrandečić and Krötzsch, 2014) are collected. We filter and combine some of the relations and propose RELX-Distant whose statistics can be seen in the table below.
+
+| **Language** | **Number of Sentences** |
+|--------------|-------------------------|
+| English      | 815689                  |
+| French       | 652842                  |
+| German       | 652062                  |
+| Spanish      | 397875                  |
+| Turkish      | 57114                   |
+
+## Citation
+
+```
+@inproceedings{koksal-ozgur-2020-relx,
+    title = "The {RELX} Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification",
+    author = {K{\"o}ksal, Abdullatif  and
+      {\"O}zg{\"u}r, Arzucan},
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.32",
+    doi = "10.18653/v1/2020.findings-emnlp.32",
+    pages = "340--350",
+}
+```