Translation
Transformers
PyTorch
Safetensors
marian
text2text-generation
opus-mt-tc-bible
Eval Results (legacy)
Instructions to use Helsinki-NLP/opus-mt-tc-bible-big-mul-mul with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-tc-bible-big-mul-mul 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="Helsinki-NLP/opus-mt-tc-bible-big-mul-mul")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-tc-bible-big-mul-mul") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-tc-bible-big-mul-mul", device_map="auto") - Notebooks
- Google Colab
- Kaggle
About Language Coverage
#2
by aoiandroid - opened
For languages with little training data, you should be able to gather a reasonable amount of data by making full use of Gemini or Azure Translator.
Please note that many of the languages listed are not well supported by this model due to very limited training data. Which languages were specifically trained with insufficient training data?