model card init

initial descriptions added in model card. Which include citation to arxiv preprint.

README.md

@@ -1,3 +1,48 @@
 ---
 license: cc-by-nc-sa-4.0
+language:
+- bn
+library_name: transformers
+tags:
+- Pytorch
+- Bengali Fake News
 ---
+# Model Description
+
+This is Bengali Fake News detection model, version 1.0. This model was introduced in [this paper](https://arxiv.org/pdf/2307.06979.pdf). 
+An original implementation is deployed in [this](https://huggingface.co/spaces/armansakif/BenFake) huggingface space.
+
+Model type: deep learning classifier
+
+Finetuned From Model : https://huggingface.co/bert-base-multilingual-cased
+## How to load this model using `transformers` (tested on 4.31.0-py3)
+```python
+from transformers import BertTokenizer, AutoTokenizer
+from transformers import BertForSequenceClassification, AdamW, BertConfig
+
+tokenizer = AutoTokenizer.from_pretrained('armansakif/bengali-fake-news')
+
+model = BertForSequenceClassification.from_pretrained(
+    "armansakif/bengali-fake-news", # Use the 12-layer BERT model, with an uncased vocab.
+    num_labels = 2, # The number of output labels--2 for binary classification.
+                    # You can increase this for multi-class tasks.
+    output_attentions = False, # Whether the model returns attentions weights.
+    output_hidden_states = False, # Whether the model returns all hidden-states.
+)
+
+```
+
+## Citation 
+If you use this model, please cite the following paper:
+BibTeX:
+```
+@article{chowdhury2023tackling,
+  title={Tackling Fake News in Bengali: Unraveling the Impact of Summarization vs. Augmentation on Pre-trained Language Models},
+  author={Chowdhury, Arman Sakif and Shahariar, GM and Aziz, Ahammed Tarik and Alam, Syed Mohibul and Sheikh, Md Azad and Belal, Tanveer Ahmed},
+  journal={arXiv preprint arXiv:2307.06979},
+  year={2023}
+}
+```
+
+### APA:
+Chowdhury, A. S., Shahariar, G. M., Aziz, A. T., Alam, S. M., Sheikh, M. A., & Belal, T. A. (2023). Tackling Fake News in Bengali: Unraveling the Impact of Summarization vs. Augmentation on Pre-trained Language Models. arXiv preprint arXiv:2307.06979.