Update README.md

README.md

@@ -6,4 +6,125 @@ base_model:
 tags:
 - nlp
 - seq2seq
----
+---
+
+# Model Card for Banglish to Bengali Transliteration using mBART
+
+This model is designed to perform transliteration from Banglish (Romanized Bengali) to Bengali script using the [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) model. The training was conducted using the dataset [SKNahin/bengali-transliteration-data](https://huggingface.co/datasets/SKNahin/bengali-transliteration-data).
+
+The notebook used for training can be found here: [Kaggle Notebook](https://www.kaggle.com/code/shadabtanjeed/mbart-banglish-to-bengali-transliteration).
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Shadab Tanjeed  
+- **Model type:** Sequence-to-sequence (Seq2Seq) Transformer model  
+- **Language(s) (NLP):** Bengali, Banglish (Romanized Bengali)  
+- **Finetuned from model:** [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt)  
+
+### Model Sources
+
+- **Repository:** [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt)  
+
+## Uses
+
+### Direct Use
+
+The model is intended for direct transliteration of Banglish text to Bengali script.
+
+### Downstream Use
+
+It can be integrated into NLP applications where transliteration from Banglish to Bengali is required, such as chatbots, text normalization, and digital content processing.
+
+### Out-of-Scope Use
+
+The model is not designed for language translation beyond transliteration, and it may not perform well on text containing mixed languages or code-switching.
+
+## Bias, Risks, and Limitations
+
+- The model may struggle with ambiguous words that have multiple possible transliterations.  
+- It may not perform well on informal or highly stylized text.  
+- Limited dataset coverage could lead to errors in transliterating uncommon words.  
+
+### Recommendations
+
+Users should validate outputs, especially for critical applications, and consider further fine-tuning if necessary.
+
+## How to Get Started with the Model
+
+```python
+from transformers import MBartForConditionalGeneration, MBartTokenizer
+
+model_name = "facebook/mbart-large-50-many-to-many-mmt"
+tokenizer = MBartTokenizer.from_pretrained(model_name)
+model = MBartForConditionalGeneration.from_pretrained(model_name)
+
+text = "ami tomake bhalobashi"
+inputs = tokenizer(text, return_tensors="pt")
+
+translated_tokens = model.generate(**inputs)
+output = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+
+print(output)  # Expected Bengali transliteration
+
+```
+
+## Training Details
+
+### Training Data
+
+The dataset used for training is [SKNahin/bengali-transliteration-data](https://huggingface.co/datasets/SKNahin/bengali-transliteration-data), which contains pairs of Banglish (Romanized Bengali) and corresponding Bengali script.
+
+### Training Procedure
+
+#### Preprocessing
+
+- Tokenization was performed using the mBART tokenizer.  
+- Text normalization techniques were applied to remove noise.  
+
+#### Training Hyperparameters
+
+- **Batch size:** 8
+- **Learning rate:** 3e-5
+- **Epochs:** 5 
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+- The same dataset [SKNahin/bengali-transliteration-data](https://huggingface.co/datasets/SKNahin/bengali-transliteration-data) was used for evaluation.  
+
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+The model follows the Transformer-based Seq2Seq architecture from mBART.
+
+
+#### Software
+
+- **Framework:** Hugging Face Transformers
+
+## Citation
+
+If you use this model, please cite the dataset and base model:
+
+```bibtex
+@inproceedings{SKNahin2023,
+  author = {SK Nahin},
+  title = {Bengali Transliteration Dataset},
+  year = {2023},
+  publisher = {Hugging Face Datasets},
+  url = {https://huggingface.co/datasets/SKNahin/bengali-transliteration-data}
+}
+
+@article{lewis2020mbart,
+  title={mBART: Multilingual Denoising Pre-training for Neural Machine Translation},
+  author={Lewis, Mike and others},
+  journal={arXiv preprint arXiv:2001.08210},
+  year={2020}
+}