Instructions to use keremp/opus-em-augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keremp/opus-em-augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keremp/opus-em-augmented")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("keremp/opus-em-augmented") model = AutoModelForSequenceClassification.from_pretrained("keremp/opus-em-augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from keremp/opus-em-augmented: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/keremp/opus-em-augmented/resolve/main/training_args.bin
- Command line
-
hf download hf://keremp/opus-em-augmented/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/keremp/opus-em-augmented/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 7f86feb114e232d0feb01a701109a04a2c831002ff6650f487e1632d2a98f027
- Size of remote file:
- 4.09 kB
- SHA256:
- 7a18dbccfa74eccf55b0960ef34055e29b8f47dfc7245c16808654b55601cf93
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