seq2seq-en-es
| Property | Value |
|---|---|
| Model Type | Sequence-to-Sequence Neural Translation |
| Architecture | Bidirectional GRU with Attention |
| License | MIT |
| Training Dataset | loresiensis/corpus-en-es |
| Final Loss | 3.527 |
What is seq2seq-en-es?
seq2seq-en-es is a PyTorch-based neural machine translation model designed specifically for English to Spanish translation. It implements a sequence-to-sequence architecture with attention mechanism, utilizing bidirectional GRU (Gated Recurrent Unit) networks for enhanced translation quality. The model incorporates modern training techniques like teacher forcing and dynamic batching to improve learning efficiency.
Implementation Details
The model architecture consists of three primary components: a bidirectional GRU encoder, an attention mechanism, and a GRU decoder. The encoder processes input sequences bidirectionally to capture context from both directions, while the attention mechanism helps focus on relevant parts of the input sequence during translation. The implementation uses the MarianTokenizer from Hugging Face for robust text processing.
- Embedding Dimensions: 256 for both encoder and decoder
- Hidden Dimensions: 512 for both encoder and decoder
- Training Batch Size: 32
- Learning Rate: 1e-3
- Training Time: ~2 hours on NVIDIA V100
Core Capabilities
- Bidirectional context processing for improved translation accuracy
- Attention mechanism for focusing on relevant input parts
- Teacher forcing implementation for stable training
- Dynamic batching support for variable sequence lengths
- Integration with Hugging Face ecosystem
Frequently Asked Questions
Q: What makes this model unique?
The model combines bidirectional GRU architecture with attention mechanism, offering a balance between translation quality and computational efficiency. Its integration with the Hugging Face ecosystem and support for dynamic batching makes it particularly suitable for production deployments.
Q: What are the recommended use cases?
This model is ideal for English to Spanish translation tasks in production environments where accuracy and efficiency are crucial. It's particularly well-suited for applications requiring real-time translation, content localization, and automated documentation translation.
