Upload README.md with huggingface_hub

README.md

@@ -1,70 +1,70 @@
----
-language:
-- en
-- fr
-- de
-- es
-- tr
-multilinguality:
-  - multilingual
-configs:
-- config_name: en
-  data_files:
-    - split: train
-      path: "RELX_distant_en.json"
-- config_name: fr
-  data_files:
-    - split: train
-      path: "RELX_distant_fr.json"
-- config_name: de
-  data_files:
-    - split: train
-      path: "RELX_distant_de.json"
-- config_name: es
-  data_files:
-    - split: train
-      path: "RELX_distant_es.json"
-- config_name: tr
-  data_files:
-    - split: train
-      path: "RELX_distant_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",
-}
+---
+language:
+- eng
+- fra
+- deu
+- spa
+- tur
+multilinguality:
+  - multilingual
+configs:
+- config_name: en
+  data_files:
+    - split: train
+      path: "RELX_distant_en.json"
+- config_name: fr
+  data_files:
+    - split: train
+      path: "RELX_distant_fr.json"
+- config_name: de
+  data_files:
+    - split: train
+      path: "RELX_distant_de.json"
+- config_name: es
+  data_files:
+    - split: train
+      path: "RELX_distant_es.json"
+- config_name: tr
+  data_files:
+    - split: train
+      path: "RELX_distant_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",
+}
 ```