Update README.md

README.md

@@ -1,3 +1,51 @@
 ---
-license: cc-by-nc-sa-3.0
+language:
+- fr
+license:
+- cc-by-nc-sa-3.0
+multilinguality:
+- monolingual
+task_categories:
+- classification
 ---
+# Dataset Card for IPC classification of French patents
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:** [IPC Classification of French Patents](https://github.com/ZoeYou/Patent-Classification-2022)
+- **Paper:** [Patent Classification using Extreme Multi-label Learning: A Case Study of French Patents](https://hal.science/hal-03850405v1)
+- **Point of Contact:** [Abigail See](you.zuo@inria.fr)
+
+### Dataset Summary
+
+INPI-CLS is a French Patents corpus extracted from the INPI internal database, and contain all parts of patent texts (title, abstract, claims, description) published from 2002 to 2021, each patent being annotated with all levels from sections to the IPC subgroup labels.
+
+### Languages
+
+French
+
+### Social Impact of Dataset
+
+The purpose of this dataset is to help develop models that enable the classification of French patents in the International Patent Classification (IPC) system standard.
+
+Due to the high integrity of the data, it can also be used for other analytical studies related to French language patents.
+
+
+### Citation Information
+
+```
+@inproceedings{zuo:hal-03850405,
+  TITLE = {{Patent Classification using Extreme Multi-label Learning: A Case Study of French Patents}},
+  AUTHOR = {Zuo, You and Mouzoun, Houda and Ghamri Doudane, Samir and Gerdes, Kim and Sagot, Beno{\^i}t},
+  URL = {https://hal.archives-ouvertes.fr/hal-03850405},
+  BOOKTITLE = {{SIGIR 2022 - PatentSemTech workshop}},
+  ADDRESS = {Madrid, Spain},
+  YEAR = {2022},
+  MONTH = Jul,
+  KEYWORDS = {IPC prediction ; Clustering and Classification ; Extreme Multi-label Learning ; French ; Patent},
+  PDF = {https://hal.archives-ouvertes.fr/hal-03850405/file/PatentSemTech_2022___extended_abstract.pdf},
+  HAL_ID = {hal-03850405},
+  HAL_VERSION = {v1},
+}
+```