diff --git a/README.md b/README.md
new file mode 100644
index 0000000..e3beda6
--- /dev/null
+++ b/README.md
@@ -0,0 +1,172 @@
+# Model Card for roberta-base-on-cuad
+  
+# Model Details
+ 
+## Model Description
+ 
+- **Developed by:** Mohammed Rakib
+- **Shared by [Optional]:** More information needed
+- **Model type:** Question Answering 
+- **Language(s) (NLP):** en
+- **License:** More information needed
+- **Related Models:**
+  - **Parent Model:** RoBERTa 
+- **Resources for more information:** 
+    - [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta)
+  - [Associated Paper](https://arxiv.org/abs/1907.11692)
+ 
+ 
+ 
+ 
+# Uses
+ 
+ 
+## Direct Use
+ 
+This model can be used for the task of  Question Answering.
+ 
+## Downstream Use [Optional]
+ 
+More information needed
+ 
+## Out-of-Scope Use
+ 
+The model should not be used to intentionally create hostile or alienating environments for people.
+ 
+# Bias, Risks, and Limitations
+ 
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
+ 
+ 
+## Recommendations
+ 
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+ 
+ 
+# Training Details
+ 
+## Training Data
+ 
+See [CUAD dataset card](https://huggingface.co/datasets/cuad) for more information.
+ 
+## Training Procedure
+ 
+ 
+### Preprocessing
+ 
+More information needed
+ 
+### Speeds, Sizes, Times
+ 
+More information needed
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+See [CUAD dataset card](https://huggingface.co/datasets/cuad) for more information.
+ 
+### Factors
+ 
+ 
+### Metrics
+ 
+More information needed
+## Results 
+ 
+More information needed
+ 
+# Model Examination
+ 
+More information needed
+ 
+# Environmental Impact
+ 
+ 
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+ 
+- **Hardware Type:** More information needed
+- **Hours used:** More information needed
+- **Cloud Provider:** More information needed
+- **Compute Region:** More information needed
+- **Carbon Emitted:** More information needed
+ 
+# Technical Specifications [optional]
+ 
+## Model Architecture and Objective
+ 
+More information needed
+ 
+## Compute Infrastructure
+ 
+More information needed
+ 
+### Hardware
+ 
+More information needed
+ 
+### Software
+More information needed
+ 
+# Citation
+ 
+ 
+**BibTeX:**
+ ```
+@article{DBLP:journals/corr/abs-1907-11692,
+  author    = {Yinhan Liu and
+               Myle Ott and
+               Naman Goyal and
+               Jingfei Du and
+               Mandar Joshi and
+               Danqi Chen and
+               Omer Levy and
+               Mike Lewis and
+               Luke Zettlemoyer and
+               Veselin Stoyanov},
+  title     = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
+  journal   = {CoRR},
+  volume    = {abs/1907.11692},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1907.11692},
+  archivePrefix = {arXiv},
+  eprint    = {1907.11692},
+  timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+ 
+ 
+# Glossary [optional]
+More information needed
+ 
+# More Information [optional]
+ 
+More information needed
+ 
+# Model Card Authors [optional]
+  
+Mohammed Rakib in collaboration with Ezi Ozoani and the Hugging Face team
+ 
+# Model Card Contact
+ 
+More information needed
+ 
+# How to Get Started with the Model
+ 
+Use the code below to get started with the model.
+ 
+<details>
+<summary> Click to expand </summary>
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+ 
+tokenizer = AutoTokenizer.from_pretrained("Rakib/roberta-base-on-cuad")
+ 
+model = AutoModelForQuestionAnswering.from_pretrained("Rakib/roberta-base-on-cuad")
+```
+</details>