Downloads · 30 days
3
12% of all-time downloads
luong-gru-multi30k
aijadugar/luong-gru-multi30k
luong-gru-multi30k is a translation model from aijadugar. Use it when you need text moved from one language to another. It is set up for pytorch. The card lists the license as mit.
A PyTorch Seq2Seq Encoder-Decoder GRU with Luong Attention trained from scratch on the Multi30k dataset.
All-time downloads
26
Public
Repo size
18.1 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors18 MB · 99%
At a glance
- Task
- Translation
- Library
- pytorch
- License
- mit
- Access
- Public
- Created
- Aug 3, 2026
- Updated
- Aug 3, 2026
- SHA
- 771eac44
Try a prompt
Datasets
Model card
From the Hugging Face model README
Seq2Seq GRU Translator (English -> German)
A PyTorch Seq2Seq Encoder-Decoder GRU with Luong Attention trained from scratch on the Multi30k dataset.
Model Details
- Architecture: Seq2Seq Encoder-Decoder GRU with Luong Attention
- Framework: PyTorch
- Dataset: Multi30k (English -> German)
- Embedding Dimension: 128
- Hidden Dimension: 256
Training
- Optimizer: Adam
- Learning Rate: 1e-3
- Loss: CrossEntropyLoss (ignore_index=PAD)
- Gradient Clipping: max_norm=1.0
- Epochs: 50
- Batch Size: 64
Dataset
| Split | Samples |
|---|---|
| Train | 29,000 |
| Validation | 1,014 |
| Test | 1,000 |
Performance
| Metric | Value |
|---|---|
| Train Loss | 0.42 |
| Validation Loss | 5.17 |
| Test Loss | 3.26 |
| BLEU | 27.13 |
Usage
# Load model
model = Seq2Seq(encoder, decoder, device)
model.load_state_dict(
load_file("best_seq2seq_luong.safetensors")
)
model.eval()
# Inference
sentence = "A man is riding a bicycle."
translation = translate_sentence(
sentence,
model
)
print(" ".join(translation))
Author Ankit Bari
GitHub: https://github.com/aijadugar Hugging Face: https://huggingface.co/aijadugar
- Task
- Translation
- Library
- pytorch
- License
- mit
- Languages
- en, de
- Created
- Aug 3, 2026
- Updated
- Aug 3, 2026