Translation
Transformers
PyTorch
TensorFlow
Safetensors
marian
text2text-generation
opus-mt-tc
Eval Results (legacy)
Instructions to use Helsinki-NLP/opus-mt-tc-big-itc-itc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-tc-big-itc-itc 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-big-itc-itc")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-tc-big-itc-itc") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-tc-big-itc-itc") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bab178bb00470f560c4652a8a1a5431e55104b4a6c2bd581e47738b3010c0729
- Size of remote file:
- 425 MB
- SHA256:
- 6fedd79272a8a490a43ae7df6d5a3c8fa1c2fb13379fda5db227f6f47a4cc808
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.