Update README.md

README.md

@@ -9,4 +9,38 @@ base_model:
 library_name: transformers
 tags:
 - legal
----
+---
+
+# BERTić-COMtext-SR-legal-lemma-ekavica
+
+**BERTić-COMtext-SR-legal-lemma-ekavica** is a variant of the [BERTić](https://huggingface.co/classla/bcms-bertic) model, fine-tuned on the task of lemmatization tag prediction in Serbian legal texts written in the Ekavian pronunciation. 
+The model was fine-tuned for 20 epochs on the Ekavian variant of the [COMtext.SR.legal](https://github.com/ICEF-NLP/COMtext.SR) dataset.
+
+# Benchmarking
+
+This model was evaluated on the task of lemmatizing Serbian legal texts.
+Lemmatization was performed using the predicted string edit tags, as described in this JTDH 2024 paper:
+* [Lemmatizing Serbian and Croatian via String Edit Prediction](https://zenodo.org/records/13937204)
+
+The model was compared to previous lemmatization approaches that relied on the [srLex](http://hdl.handle.net/11356/1233) inflectional lexicon:
+- The [CLASSLA](http://pypi.org/project/classla/) library
+- A variant of [BERTić](https://huggingface.co/classla/bcms-bertic) fine-tuned for MSD prediction using the [SETimes.SR 2.0](http://hdl.handle.net/11356/1843) corpus of newswire texts
+- A [variant](https://huggingface.co/ICEF-NLP/bcms-bertic-comtext-sr-legal-msd-ekavica) of [BERTić](https://huggingface.co/classla/bcms-bertic) fine-tuned for MSD prediction using the [COMtext.SR.legal](https://github.com/ICEF-NLP/COMtext.SR) corpus of legal texts
+- [SrBERTa](http://huggingface.co/nemanjaPetrovic/SrBERTa), a model specially trained on Serbian legal texts, fine-tuned for MSD prediction using the [COMtext.SR.legal](https://github.com/ICEF-NLP/COMtext.SR) corpus of legal texts
+
+Accuracy was used as the evaluation metric and gold tokenized text was taken as input.
+All of the previous large language models were fine-tuned for 15 epochs.
+CLASSLA and BERTić-SETimes were directly tested on the entire COMtext.SR.legal.ekavica corpus.
+BERTić-COMtext-SR-legal-MSD-ekavica, BERTić-COMtext-SR-legal-lemma-ekavica, and SrBERTa were fine-tuned and evaluated on the COMtext.SR.legal.ekavica corpus using 10-fold CV.
+
+The code and data to run these experiments is available on the [COMtext.SR GitHub repository](https://github.com/ICEF-NLP/COMtext.SR).
+
+## Results
+
+| Model                                     | Lemma ACC  |
+| ----------------------------------------- | ---------- |
+| CLASSLA-SR                                |   0.9432   |
+| BERTić-SETimes                            |   0.9649   |
+| BERTić-COMtext-SR-legal-MSD-ekavica       |   0.9666   |
+| SrBERTa                                   |   0.9391   |
+| **BERTić-COMtext-SR-legal-lemma-ekavica** | **0.9850** |