Update README.md

README.md

@@ -1,41 +1,82 @@
 ---
 license: cc-by-nc-4.0
 base_model: facebook/nllb-200-1.3B
-tags:
-- generated_from_trainer
 metrics:
 - bleu
 model-index:
 - name: Terjman-Ultra
   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-Ultra
+# Terjman-Ultra (1.3B)
+
+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 [facebook/nllb-200-1.3B](https://huggingface.co/facebook/nllb-200-1.3B) 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 [facebook/nllb-200-1.3B](https://huggingface.co/facebook/nllb-200-1.3B) on an unknown dataset.
 It achieves the following results on the evaluation set:
 - Loss: 2.7070
 - Bleu: 4.6998
 - Gen Len: 35.6088
 
-## Model description
+The finetuning was conducted using a **A100-40GB** and took **32 hours**. 
+
+Try it out on our dedicated [Terjman-Ultra Space](https://huggingface.co/spaces/atlasia/Terjman-Ultra) 🤗
+
+## Usage
+
+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:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("atlasia/Terjman-Ultra")
+model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Terjman-Ultra")
+
+# Define your Moroccan Darija Arabizi text
+input_text = "Your english text goes here."
+
+# Tokenize the input text
+input_tokens = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
+
+# 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:
 
-More information needed
+**Input**: "Hi my friend, can you tell me a joke in moroccan darija? I'd be happy to hear that from you!"
 
-## Intended uses & limitations
+**Output**: "أهلا صاحبي، تقدر تقولي مزحة بالدارجة المغربية؟ غادي نكون فرحان باش نسمعها منك!"
 
-More information needed
+## Limiations
 
-## Training and evaluation data
+This version has some limitations mainly due to the Tokenizer.
+We're currently collecting more data with the aim of continous improvements.
 
-More information needed
+## Feedback
 
-## Training procedure
+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
+## Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 3e-05
@@ -49,7 +90,7 @@ The following hyperparameters were used during training:
 - lr_scheduler_warmup_ratio: 0.03
 - num_epochs: 25
 
-### Training results
+## Training results
 
 | Training Loss | Epoch   | Step  | Validation Loss | Bleu   | Gen Len |
 |:-------------:|:-------:|:-----:|:---------------:|:------:|:-------:|
@@ -80,7 +121,7 @@ The following hyperparameters were used during training:
 | 2.8129        | 24.9972 | 56050 | 2.7070          | 4.6998 | 35.6088 |
 
 
-### Framework versions
+## Framework versions
 
 - Transformers 4.40.2
 - Pytorch 2.2.1+cu121