Update README.md

README.md

@@ -161,4 +161,93 @@ configs:
     path: only_english_lowlevel/train-*
   - split: validation
     path: only_english_lowlevel/validation-*
+task_categories:
+- text-classification
+language:
+- en
+- es
+- pl
+- hu
+- el
+- da
+- tr
+- ja
+- sv
+- fi
+- 'no'
+- ru
+- it
+- he
+- is
+tags:
+- finance
+size_categories:
+- 1K<n<10K
 ---
+
+# MultiFin
+
+<!-- Provide a quick summary of the dataset. -->
+
+MultiFin – a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families.
+The dataset consists of hierarchical label structure providing two classification tasks: multi-label and multi-class.
+## Dataset Details
+
+### Dataset Description
+
+The MULTIFIN dataset is a multilingual corpus, consisting of real-world article headlines covering 15
+languages. The corpus is annotated using hierarchical label structure, providing two classification tasks:
+multi-class and multi-label classification.
+
+
+- **Curated by:** Rasmus Jørgensen, Oliver Brandt, Mareike Hartmann, Xiang Dai, Christian Igel, and Desmond Elliott.
+- **Language(s) (NLP):** English, Spanish, Polish, Hungarian, Greek, Danish, Turkish, Japanese, Swedish, Finnish, Norwegian, Russian, Italian, Hebrew, Icelandic.
+- **License:** [More Information Needed]
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://github.com/RasmusKaer/MultiFin
+- **Paper:** https://aclanthology.org/2023.findings-eacl.66/
+
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+[More Information Needed]
+
+
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+@inproceedings{jorgensen-etal-2023-multifin,
+    title = "{M}ulti{F}in: A Dataset for Multilingual Financial {NLP}",
+    author = "J{\o}rgensen, Rasmus  and
+      Brandt, Oliver  and
+      Hartmann, Mareike  and
+      Dai, Xiang  and
+      Igel, Christian  and
+      Elliott, Desmond",
+    editor = "Vlachos, Andreas  and
+      Augenstein, Isabelle",
+    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
+    month = may,
+    year = "2023",
+    address = "Dubrovnik, Croatia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.findings-eacl.66",
+    doi = "10.18653/v1/2023.findings-eacl.66",
+    pages = "894--909",
+    abstract = "Financial information is generated and distributed across the world, resulting in a vast amount of domain-specific multilingual data. Multilingual models adapted to the financial domain would ease deployment when an organization needs to work with multiple languages on a regular basis. For the development and evaluation of such models, there is a need for multilingual financial language processing datasets. We describe MultiFin {--} a publicly available financial dataset consisting of real-world article headlines covering 15 languages across different writing systems and language families. The dataset consists of hierarchical label structure providing two classification tasks: multi-label and multi-class. We develop our annotation schema based on a real-world application and annotate our dataset using both {`}label by native-speaker{'} and {`}translate-then-label{'} approaches. The evaluation of several popular multilingual models, e.g., mBERT, XLM-R, and mT5, show that although decent accuracy can be achieved in high-resource languages, there is substantial room for improvement in low-resource languages.",
+}
+
+
+
+
+