Instructions to use RinKana/eng-jpn-transformer-nmt-efficient-63M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use RinKana/eng-jpn-transformer-nmt-efficient-63M with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://RinKana/eng-jpn-transformer-nmt-efficient-63M") - Notebooks
- Google Colab
- Kaggle
English-Japanese Transformer (65M Parameters)
A high-capacity Transformer model for English → Japanese translation, trained on 1 Million sentence pairs from the Sampuran dataset. This model balances high capacity ($pprox 65$ Million parameters) with aggressive regularization (Dropout $\mathbf0.45$) to ensure generalization across a large dataset.
Performance
- Model Size: 65,324,816 parameters
- Training Data: 1 Million pairs
- Average Character BLEU Score (Test Set): 0.0836
- Best Validation Loss: 0.9559
- Final Training Epochs: 4 (Stopped by EarlyStopping)
Usage
The model can be loaded and used for inference with the included tokenizers.
import keras
from huggingface_hub import hf_hub_download
# Download model file
model_path = hf_hub_download(repo_id="RinKana/eng-jpn-transformer-nmt-efficient-63M", filename="transformer_model.keras")
model = keras.models.load_model(model_path)
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# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://RinKana/eng-jpn-transformer-nmt-efficient-63M")