|
--- |
|
license: cc-by-nc-4.0 |
|
base_model: facebook/nllb-200-3.3B |
|
metrics: |
|
- bleu |
|
model-index: |
|
- name: Terjman-Supreme |
|
results: [] |
|
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-Supreme (3.3B) |
|
|
|
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-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) on a the [darija_english](atlasia/darija_english) dataset enhanced with curated corpora ensuring high-quality and accurate translations. |
|
|
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.3687 |
|
- Bleu: 5.6718 |
|
- Gen Len: 39.9504 |
|
|
|
The finetuning was conducted using a **A100-40GB** and took **57 hours**. |
|
|
|
Try it out on our dedicated [Terjman-Supreme Space](https://huggingface.co/spaces/atlasia/Terjman-Supreme) 🤗 |
|
|
|
## 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-Supreme") |
|
model = AutoModelForSeq2SeqLM.from_pretrained("atlasia/Terjman-Supreme") |
|
|
|
# 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: |
|
|
|
**Input**: "Hi my friend, can you tell me a joke in moroccan darija? I'd be happy to hear that from you!" |
|
|
|
**Output**: "أهلا صاحبي، واش تقدر تقول لي نكتة بالدارجة المغربية؟ غادي نكون فرحان باش نسمعها منك!" |
|
|
|
## Limitations |
|
|
|
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 |
|
- train_batch_size: 1 |
|
- eval_batch_size: 1 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 4 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_ratio: 0.03 |
|
- num_epochs: 25 |
|
|
|
## Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
|
|:-------------:|:-------:|:------:|:---------------:|:------:|:-------:| |
|
| 2.7591 | 1.0000 | 8968 | 2.5401 | 4.6975 | 39.4298 | |
|
| 2.593 | 1.9999 | 17936 | 2.4204 | 5.4569 | 40.4573 | |
|
| 2.6088 | 2.9999 | 26904 | 2.3929 | 5.5729 | 40.7879 | |
|
| 2.5744 | 4.0 | 35873 | 2.3821 | 5.567 | 40.27 | |
|
| 2.5658 | 5.0000 | 44841 | 2.3752 | 5.6589 | 40.3085 | |
|
| 2.5786 | 5.9999 | 53809 | 2.3725 | 5.5713 | 39.9532 | |
|
| 2.5197 | 6.9999 | 62777 | 2.3705 | 5.5688 | 39.9642 | |
|
| 2.5599 | 8.0 | 71746 | 2.3703 | 5.644 | 39.9449 | |
|
| 2.5763 | 9.0000 | 80714 | 2.3692 | 5.5916 | 40.3581 | |
|
| 2.5734 | 9.9999 | 89682 | 2.3695 | 5.6339 | 40.0248 | |
|
| 2.5461 | 10.9999 | 98650 | 2.3689 | 5.6151 | 40.0055 | |
|
| 2.5712 | 12.0 | 107619 | 2.3690 | 5.5447 | 40.3554 | |
|
| 2.5338 | 13.0000 | 116587 | 2.3685 | 5.6284 | 40.0138 | |
|
| 2.5733 | 13.9999 | 125555 | 2.3693 | 5.7858 | 40.4105 | |
|
| 2.5621 | 14.9999 | 134523 | 2.3684 | 5.6093 | 39.9614 | |
|
| 2.5205 | 16.0 | 143492 | 2.3685 | 5.6545 | 40.3444 | |
|
| 2.5815 | 17.0000 | 152460 | 2.3686 | 5.7027 | 40.3416 | |
|
| 2.5666 | 17.9999 | 161428 | 2.3686 | 5.6684 | 39.9284 | |
|
| 2.5721 | 18.9999 | 170396 | 2.3683 | 5.6005 | 40.0028 | |
|
| 2.5325 | 20.0 | 179365 | 2.3685 | 5.688 | 40.3223 | |
|
| 2.5385 | 21.0000 | 188333 | 2.3684 | 5.6137 | 39.989 | |
|
| 2.5399 | 21.9999 | 197301 | 2.3681 | 5.6324 | 40.3499 | |
|
| 2.5501 | 22.9999 | 206269 | 2.3679 | 5.6608 | 39.9725 | |
|
| 2.5419 | 24.0 | 215238 | 2.3679 | 5.6112 | 40.3196 | |
|
| 2.5583 | 24.9993 | 224200 | 2.3687 | 5.6718 | 39.9504 | |
|
|
|
|
|
## Framework versions |
|
|
|
- Transformers 4.40.2 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
