Instructions to use Mitsua/elan-mt-base-en-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mitsua/elan-mt-base-en-ja with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Mitsua/elan-mt-base-en-ja")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Mitsua/elan-mt-base-en-ja") model = AutoModelForSeq2SeqLM.from_pretrained("Mitsua/elan-mt-base-en-ja") - Notebooks
- Google Colab
- Kaggle
ElanMT
This model is a pretrained checkpoint and is suitable for fine-tuning on a large dataset. For general use cases, using ElanMT-BT-en-ja is strongly recommended.
Model Details
This is a translation model based on Marian MT 6-layer encoder-decoder transformer architecture with sentencepiece tokenizer.
- Developed by: ELAN MITSUA Project / Abstract Engine
- Model type: Translation
- Source Language: English
- Target Language: Japanese
- License: CC BY-SA 4.0
Usage
Training Data
Training Procedure
Evaluation
Disclaimer
The translated result may be very incorrect, harmful or biased. The model was developed to investigate achievable performance with only a relatively small, licensed corpus, and is not suitable for use cases requiring high translation accuracy. Under Section 5 of the CC BY-SA 4.0 License, ELAN MITSUA Project / Abstract Engine is not responsible for any direct or indirect loss caused by the use of the model.
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Dataset used to train Mitsua/elan-mt-base-en-ja
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