metadata
license: mit
language:
- en
- de
library_name: pytorch
pipeline_tag: translation
tags:
- pytorch
- gru
- seq2seq
- machine-translation
- multi30k
datasets:
- bentrevett/multi30k
Seq2Seq GRU Translator (English -> German)
A PyTorch Seq2Seq Encoder-Decoder GRU trained from scratch on the Multi30k dataset.
Model Details
- Architecture: Seq2Seq
- 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 | 0.87 |
| Validation Loss | 5.19 |
| Test Loss | 3.75 |
| BLEU | 16.06 |
Usage
# Load model
model = Seq2Seq(encoder, decoder, device)
model.load_state_dict(
load_file("best_seq2seq_gru.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