Instructions to use Mitsua/elan-mt-tiny-en-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mitsua/elan-mt-tiny-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-tiny-en-ja")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Mitsua/elan-mt-tiny-en-ja") model = AutoModelForSeq2SeqLM.from_pretrained("Mitsua/elan-mt-tiny-en-ja", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download README.md from Mitsua/elan-mt-tiny-en-ja: direct link, hf CLI and curl.
- Browser
- Download file 1.69 kB
-
https://huggingface.co/Mitsua/elan-mt-tiny-en-ja/resolve/main/README.md
- Command line
-
hf download hf://Mitsua/elan-mt-tiny-en-ja/README.md
-
curl -L -o README.md https://huggingface.co/Mitsua/elan-mt-tiny-en-ja/resolve/main/README.md
license: cc-by-sa-4.0
datasets:
- Mitsua/wikidata-parallel-descriptions-en-ja
language:
- ja
- en
metrics:
- bleu
- chrf
library_name: transformers
pipeline_tag: translation
ElanMT
This model is a tiny variant of ElanMT-BT-en-ja and is trained from scratch exclusively on openly licensed data and Wikipedia back translated data using ElanMT-base-ja-en.
Model Details
This is a translation model based on Marian MT 4-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.