ft-tatoeba-ar-en
| Property | Value |
|---|---|
| Author | abdusah |
| Training Data | Open Subtitles Dataset |
| Framework | PyTorch 1.10.2+cu113 |
| Hugging Face | Model Repository |
What is ft-tatoeba-ar-en?
ft-tatoeba-ar-en is a machine translation model specifically designed for Arabic-to-English translation. The model was trained from scratch on the Open Subtitles dataset, utilizing modern deep learning techniques and optimizations for neural machine translation.
Implementation Details
The model implements a neural architecture trained with carefully selected hyperparameters. It uses the Adam optimizer with betas=(0.9,0.999) and epsilon=1e-08, combined with a linear learning rate scheduler. Training was conducted with a learning rate of 2e-05 and batch sizes of 16 for both training and evaluation.
- Native AMP (Automatic Mixed Precision) training for improved performance
- Single epoch training with seed 42 for reproducibility
- Implemented using Transformers 4.18.0.dev0 and Datasets 1.18.4
Core Capabilities
- Arabic to English translation
- Optimized for subtitle-style content
- Efficient processing with mixed precision training
- Production-ready with standard batch processing
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its specific focus on Arabic-English translation, trained on the Open Subtitles dataset, making it particularly effective for conversational and subtitle-style translations. The implementation of native AMP training and careful hyperparameter selection ensures efficient processing and good translation quality.
Q: What are the recommended use cases?
The model is best suited for translating Arabic to English content, particularly in contexts similar to subtitle text and conversational language. It's ideal for applications involving media translation, subtitle generation, and general Arabic-English translation tasks.
