Update README.md

README.md

@@ -12,4 +12,29 @@ widget:
   - "Szép az autó."
   - "Elutazok egy napra."
   example_title: "Példa"
----
+---
+
+# Hungarian Experimental Sentence-BERT
+
+The pre-trained hubert-base-cc[https://huggingface.co/SZTAKI-HLT/hubert-base-cc] was fine-tuned on the Hunglish 2.0[http://mokk.bme.hu/resources/hunglishcorpus/] parallel corpus to mimic the bert-base-nli-stsb-mean-tokens[https://huggingface.co/sentence-transformers/bert-base-nli-stsb-mean-tokens] model provided by UKPLab. Sentence embeddings were obtained by applying mean pooling to the huBERT output. The data was split into training (98%) and validation (2%) sets. By the end of the training, a mean squared error of 0.106 was computed on the validation set. Our code was based on the Sentence-Transformers[https://www.sbert.net] library. Our model was trained for 2 epochs on a single GTX 1080Ti GPU card with the batch size set to 32. The training took approximately 15 hours.
+
+## Limitations
+
+- max_seq_length = 128
+
+## Usage
+
+## Citation
+If you use this model, please cite the following paper:
+
+```
+@article {bertopic,
+    title = {Analyzing Narratives of Patient Experiences: A BERT Topic Modeling Approach},
+    journal = {Acta Polytechnica Hungarica},
+    year = {2023},
+    author = {Osváth, Mátyás and Yang, Zijian Győző and Kósa, Karolina},
+    pages = {153--171},
+    volume = {20},
+    number = {7}
+}
+```