Translation
Transformers
PyTorch
TensorFlow
Safetensors
marian
text2text-generation
opus-mt-tc
Eval Results (legacy)
Instructions to use Helsinki-NLP/opus-mt-tc-base-gmw-gmw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-tc-base-gmw-gmw 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-base-gmw-gmw")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-tc-base-gmw-gmw") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-tc-base-gmw-gmw") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9fb18572642331b9cb0e04c7d6c239593e0f4a0abbad9502dbd9ff047a948a21
- Size of remote file:
- 30.8 MB
- SHA256:
- 3a99fdfc9a2526feb7568e5e87cefd4fb1417c9cbae893733eb24928f99d351d
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