Update README.md

README.md

@@ -10,30 +10,16 @@ model-index:
   results: []
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-<!-- 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-Large
 
-This model is a fine-tuned version of [Helsinki-NLP/opus-mt-tc-big-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-en-ar) on an unknown dataset.
+Our model is built upon the powerful Transformer architecture, leveraging state-of-the-art natural language processing techniques. 
+It has been finetuned on a the "atlasia/darija_english" dataset enhanced with curated corpora ensuring high-quality and accurate translations.
+
 It achieves the following results on the evaluation set:
 - Loss: 3.2078
 - Bleu: 8.3292
 - Gen Len: 34.4959
 
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
 
 ### Training hyperparameters
 
@@ -100,4 +86,51 @@ The following hyperparameters were used during training:
 - Transformers 4.40.2
 - Pytorch 2.2.1+cu121
 - Datasets 2.19.1
-- Tokenizers 0.19.1
+- Tokenizers 0.19.1
+
+
+## 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-Large")
+model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Terjman-Large")
+
+# 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("Transliteration:", output_text)
+```
+
+## Example
+
+Let's see an example of transliterating Moroccan Darija Arabizi to Arabic:
+
+**Input**: "Hello my friend, how's life in Morocco"
+
+**Output**: "مرحبا يا صاحبي, كيفاش الحياة فالمغرب"
+
+## 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.