Instructions to use Helsinki-NLP/opus-mt-en-mos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-en-mos 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-en-mos")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-mos") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-mos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from Helsinki-NLP/opus-mt-en-mos: direct link, hf CLI and curl.
- Browser
- Download file 276 MB
-
https://huggingface.co/Helsinki-NLP/opus-mt-en-mos/resolve/main/tf_model.h5
- Command line
-
hf download hf://Helsinki-NLP/opus-mt-en-mos/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Helsinki-NLP/opus-mt-en-mos/resolve/main/tf_model.h5
276 MB
- Xet hash:
- e05ef55337227db9234e55941de36d1261cdd2696b8de3999b76286757d9ef68
- Size of remote file:
- 276 MB
- SHA256:
- 2317fbf8d3d0d5f6eeb4d3f5787481c4fae2df2db62bbeef41f546811a68e7f6
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