Create README.md

README.md

@@ -0,0 +1,45 @@
+---
+
+language: 
+  - hu
+tags:
+- text-classification
+license: gpl
+metrics:
+- accuracy
+widget:
+- text: "Jó reggelt! majd küldöm az élményhozókat:)."
+
+---
+
+# Hungarian Sentence-level Sentiment Analysis model with huBERT
+
+  - Pretrained model used: huBERT
+  - Finetuned on Hungarian Twitter Sentiment (HTS) Corpus
+  	
+## Limitations
+
+- max_seq_length = 128
+
+## Results
+
+| Model | HTS2 | HTS5 |
+| ------------- | ------------- | ------------- |
+| huBERT | **85.55** | 68.99  |
+| XLM-RoBERTa| 85.56 | 85.56 |
+
+## Citation
+If you use this model, please cite the following paper:
+
+```
+@inproceedings {yang-bart,
+    title = {Improving Performance of Sentence-level Sentiment Analysis with Data Augmentation Methods},
+	booktitle = {Proceedings of 12th IEEE International Conference on Cognitive Infocommunications (CogInfoCom 2021)},
+	year = {2021},
+	publisher = {IEEE},
+	address = {Online},
+	author = {{Laki, László and Yang, Zijian Győző}}
+	pages = {417--422}
+}
+
+```