Update README.md

README.md

@@ -6,36 +6,73 @@ tags:
 metrics:
 - bleu
 model-index:
-- name: Terjman-Nano-MAX_LEN-512
+- name: Terjman-Nano
   results: []
+datasets:
+- atlasia/darija_english
+language:
+- ar
+- en
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Terjman-Nano (77M params)
 
-# Terjman-Nano-MAX_LEN-512
+Our model is built upon the powerful Transformer architecture, leveraging state-of-the-art natural language processing techniques.
+It is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on a the [darija_english](atlasia/darija_english) dataset enhanced with curated corpora ensuring high-quality and accurate translations.
 
-This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on an unknown dataset.
 It achieves the following results on the evaluation set:
 - Loss: 3.2038
 - Bleu: 10.6239
 - Gen Len: 35.2727
 
-## Model description
 
-More information needed
+## Usage
 
-## Intended uses & limitations
+Using our model for translation 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:
 
-More information needed
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-## Training and evaluation data
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("atlasia/Terjman-Nano")
+model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Terjman-Nano")
 
-More information needed
+# Define your Moroccan Darija Arabizi text
+input_text = "Your english text goes here."
 
-## Training procedure
+# Tokenize the input text
+input_tokens = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
 
-### Training hyperparameters
+# Perform translation
+output_tokens = model.generate(**input_tokens)
+
+# Decode the output tokens
+output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+
+print("Translation:", output_text)
+```
+
+## Example
+
+Let's see an example of transliterating Moroccan Darija Arabizi to Arabic:
+
+**Input**: "Hi my friend, can you tell me a joke in moroccan darija? I'd be happy to hear that from you!"
+
+**Output**: "مرحبا يا صديقي، يمكن تقال لي نكتة فالداريا المغاربية؟ أنا سَأكُونُ سعيد بسمْاع هادشي منك!"
+
+## 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.
+
+## Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 3e-05
@@ -49,7 +86,7 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.03
 - num_epochs: 40
 
-### Training results
+## Training results
 
 | Training Loss | Epoch   | Step | Validation Loss | Bleu    | Gen Len |
 |:-------------:|:-------:|:----:|:---------------:|:-------:|:-------:|
@@ -95,7 +132,7 @@ The following hyperparameters were used during training:
 | 3.524         | 39.9287 | 5600 | 3.2038          | 10.6239 | 35.2727 |
 
 
-### Framework versions
+## Framework versions
 
 - Transformers 4.40.2
 - Pytorch 2.2.1+cu121