Update README.md

README.md

@@ -4,6 +4,10 @@ tags:
 model-index:
 - name: Transliteration-Moroccan-Darija
   results: []
+datasets:
+- atlasia/ATAM
+language:
+- ar
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -11,23 +15,16 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Transliteration-Moroccan-Darija
 
-This model was trained from scratch on an unknown dataset.
+This model is trained to convert Moroccan Darija text written in Arabizi (Latin script) to Arabic letters. 
+Whether you're dealing with informal texts, social media posts, or any other content in Moroccan Arabizi, the model is here to help you accurately transliterate it into Arabic script.
 
-## Model description
+## Model Overview
 
-More information needed
+Our model is built upon the powerful Transformer architecture, leveraging state-of-the-art natural language processing techniques. 
+It has been trained from scratch on the "atlasia/ATAM" dataset, specifically for the task of transliterating Moroccan Darija Arabizi into Arabic letters, ensuring high-quality and accurate transliterations.
+Furthermore, we trained a BPE Tokenizer specifically for this task.
 
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
+## Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 3e-05
@@ -41,9 +38,57 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.02
 - num_epochs: 120
 
-### Framework versions
+## Framework versions
 
 - Transformers 4.39.2
 - Pytorch 2.2.2+cpu
 - Datasets 2.18.0
 - Tokenizers 0.15.2
+  
+## Usage
+
+Using our model for transliteration is simple and straightforward. 
+You can integrate it into your projects or workflows via the Hugging Face Transformers library. 
+Here's a basic example of how to use the model in Python:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("BounharAbdelaziz/Transliteration-Moroccan-Darija")
+model = AutoModelForSeq2SeqLM.from_pretrained("BounharAbdelaziz/Transliteration-Moroccan-Darija")
+
+# Define your Moroccan Darija Arabizi text
+input_text = "Your Moroccan Darija Arabizi text goes here."
+
+# Tokenize the input text
+input_tokens = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
+
+# Perform transliteration
+output_tokens = model.generate(**input_tokens)
+
+# Decode the output tokens
+output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+
+print("Transliteration:", output_text)
+```
+
+## Example
+
+Let's see an example of transliterating Moroccan Darija Arabizi to Arabic:
+
+**Input**: "kayn chi"
+
+**Output**: "كاين شي"
+
+
+## Limiations
+
+This version has some limitations mainly due to the Tokenizer.
+We're currently collecting more data with the aim of continous improvements
+
+## Feedback
+
+We're continuously striving to improve our model's performance and usability and we will be improving it incrementaly. 
+If you have any feedback, suggestions, or encounter any issues, please don't hesitate to reach out to us.
+