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--- |
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library_name: transformers |
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language: |
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- en |
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metrics: |
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- bleu |
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pipeline_tag: translation |
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--- |
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# Model Card for Model ID |
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Model Card for English-to-Darija Translation (mBART Fine-tuned Model) |
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## Model Details |
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### Model Description |
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This model is a fine-tuned version of the facebook/mbart-large-50-many-to-many-mmt model, |
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specifically tailored for translating English text to Moroccan Darija in Arabic script. |
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The model was trained on a custom dataset of English-Darija sentence pairs, |
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and it has been designed to accurately capture the nuances of the Moroccan dialect. |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** Aicha Lahnouki |
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- **Finetuned from model:** facebook/mbart-large-50-many-to-many-mmt |
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- **Model type:** Sequence-to-Sequence Translation (mBART architecture) |
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- **Language(s) (NLP):** English (en_XX), Darija (ar_AR) |
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## Uses |
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### Direct Use |
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This model is intended for translating English sentences into Moroccan Darija in Arabic script. |
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It can be used in applications such as translation services, language learning tools, or chatbots. |
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## Bias, Risks, and Limitations |
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This model was trained on 50% of the dataset provided by DODa, consisting of 45,000 rows. |
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The testing was conducted on a sample of 100 sentences. Due to the reduced training data, |
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the model might not capture the full linguistic diversity of English-to-Darija translations. |
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Additionally, the limited test size may not fully represent the model's performance across all possible inputs, |
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leading to potential biases or inaccuracies when applied to unseen or diverse data. |
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## How to Get Started with the Model |
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You can start using the model for English-to-Darija translation with the following code: |
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```python |
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from transformers import pipeline |
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# Initialize the translation pipeline |
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pipe = pipeline("translation", model="alpha2002/eng_alpha_darija", tokenizer="alpha2002/eng_alpha_darija") |
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# Translate English to Darija |
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input_text = "Hello, how are you?" |
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translation = pipe(input_text, src_lang="en_XX", tgt_lang="ar_AR") |
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print("Translation:", translation[0]['translation_text']) |
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``` |
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## Training Details |
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### Training Data |
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The model was trained on a custom dataset containing parallel English and Darija sentences. |
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The dataset was preprocessed to include language tokens specific to mBART's requirements. |
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### Training Procedure |
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#### Preprocessing [optional] |
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The English text was tokenized with the <en_XX> token, and the Darija text with the <ar_AR> token. |
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#### Training Hyperparameters |
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- **Training regime:** FP16 mixed precision was used during training to improve performance. |
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Training was done on Google Colab using a subset of the data, with gradient accumulation to handle larger batch sizes. |
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#### Speeds, Sizes, Times [optional] |
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The model was trained for 2 epochs with a batch size of 4, using the Seq2SeqTrainer from the Hugging Face Transformers library. |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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The model was evaluated on a small set of held-out test sentences: 100 samples. |
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#### Metrics |
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BLEU score was used to measure translation accuracy. |
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### Results |
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The model achieved a BLEU score of 11.6 on the test set, |
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indicating a reasonable level of accuracy given the complexity of translating between languages with different scripts and linguistic structures. |
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## Environmental Impact |
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- **Hardware Type:** Google Colab GPU (NVIDIA Tesla K80) |
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- **Hours used:** Approximately 2 hours for training and 1hour for testing. |
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## Citation [optional] |
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**BibTeX:** |
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@misc{lahnouki2024eng_alpha_darija, |
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author = {Aicha Lahnouki}, |
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title = {English-to-Darija Translation Model}, |
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year = {2024}, |
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url = {https://huggingface.co/alpha2002/eng_alpha_darija}, |
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} |
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## Model Card Authors [optional] |
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Lahnouki Aicha |
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## Model Card Contact |
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email: aichalahnouki@gmail.com |
