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atlasia
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Terjman-Large-v2

Text2Text Generation
Transformers
Safetensors
Arabic
English
marian
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Terjman-Large-v2
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---
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 translations.


## 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 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**: "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.