Instructions to use lachkarsalim/LatinDarija_English-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lachkarsalim/LatinDarija_English-v2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="lachkarsalim/LatinDarija_English-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lachkarsalim/LatinDarija_English-v2") model = AutoModelForSeq2SeqLM.from_pretrained("lachkarsalim/LatinDarija_English-v2") - Notebooks
- Google Colab
- Kaggle
license: apache-2.0 base_model: Helsinki-NLP/opus-mt-ar-en
This model's role is to translate Daraija with Latin words or Arabizi into English. It was trained on 170,000 rows of translation examples.
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ar-en on anDarija Open Dataset (DODa), an ambitious open-source project dedicated to the Moroccan dialect. With about 150,000 entries, DODa is arguably the largest open-source collaborative project for Darija <=> English translation built for Natural Language Processing purposes.
Training hyperparameters
The following hyperparameters were used during training:
- GPU : H100 80GB SXM5
- train_batch_size: 32
- eval_batch_size: 32
- num_epochs: 5
- mixed_precision_training: True FP16 enabled
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Model tree for lachkarsalim/LatinDarija_English-v2
Base model
Helsinki-NLP/opus-mt-ar-en