Instructions to use staka/takomt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use staka/takomt 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="staka/takomt")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("staka/takomt") model = AutoModelForSeq2SeqLM.from_pretrained("staka/takomt") - Notebooks
- Google Colab
- Kaggle
TakoMT
This is a translation model using Marian-NMT. For more details, please see my repository.
In addition to the data listed in the repository I also used ParaCrawl.
- source languages: de, en, es, fr, it, ru, uk
- target language: ja
How to use
This model uses transformers and sentencepiece.
!pip install transformers sentencepiece
You can use this model directly with a pipeline:
from transformers import pipeline
tako_translator = pipeline('translation', model='staka/takomt')
tako_translator('This is a cat.')
Eval results
The results of the evaluation using tatoeba(randomly selected 500 sentences) are as follows:
| source | target | BLEU(*1) |
|---|---|---|
| de | ja | 27.8 |
| en | ja | 28.4 |
| es | ja | 32.0 |
| fr | ja | 27.9 |
| it | ja | 24.3 |
| ru | ja | 27.3 |
| uk | ja | 29.8 |
(*1) sacrebleu --tokenize ja-mecab
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