Create README.md

README.md

@@ -0,0 +1,89 @@
+---
+license: cc-by-nc-4.0
+base_model: Helsinki-NLP/opus-mt-tc-big-en-ar
+model-index:
+- name: Terjman-Large-v2
+  results: []
+datasets:
+- atlasia/darija_english
+language:
+- ar
+---
+
+# Transliteration-Moroccan-Darija
+
+This model is trained to translate English text (en) into Moroccan Darija text (Ary) written in Arabic letters. 
+
+## Model Overview
+
+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 transliterations.
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-04
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 30
+
+## Framework versions
+
+- Transformers 4.39.2
+- Pytorch 2.2.2+cpu
+- Datasets 2.18.0
+- Tokenizers 0.15.2
+
+## 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-v2")
+model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Terjman-Large-v2")
+
+# 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 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**: "Hello my friend, how's life in Morocco"
+
+**Output**: "سالام صاحبي كيف الأحوال 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.