Update README.md

README.md

@@ -17,10 +17,18 @@ We have conducted rigorous experimentation using multiple deep learning and pre-
 language models to develop a reliable detection system. Finally, we propose a weighted ensemble model 
 that combines four pre-trained transformers: *[BanglaBERT](https://huggingface.co/csebuetnlp/banglabert), [BanglaBERT Base](https://huggingface.co/sagorsarker/bangla-bert-base), [BanglaBERT Large](https://huggingface.co/csebuetnlp/banglabert_large)* and *[BanglaBERT Generator](https://huggingface.co/csebuetnlp/banglabert_generator)*.
 
-- The paper **"Bengali Fake Reviews: A Benchmark Dataset and Detection System"** is published in [Neurocomputing](https://www.sciencedirect.com/journal/neurocomputing), a **Q1 journal** by Elsevier (Impact Factor 6).
-
 - **Paper Link**: https://www.sciencedirect.com/science/article/abs/pii/S0925231224005034
 
+# Fine tuned Bangla BERT Model
+This model is basically a fine tuned [Bangla BERT](https://huggingface.co/csebuetnlp/banglabert) model on 13390 reviews, of which 6695 were fake (1339 were genuine fakes, while the remaining 6695 were
+augmented using [nlpaug](https://pypi.org/project/nlpaug/0.0.5/) augmentation technique and 6695 were non-fake (randomly chosen from 7710 cases) from the BFRD dataset.
+
+# BFRD Dataset
+- **HuggingFace**: https://huggingface.co/datasets/shawon95/Bengali-Fake-Review-Dataset
+
+- **Kaggle**: https://www.kaggle.com/datasets/shawontanvir/bengali-fake-review-dataset
+	
+- **paperswithcode**: https://paperswithcode.com/dataset/bfrd
 
 ## Using this model as a discriminator in `transformers`