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 pytorch_model.bin from keremp/opus-em-augmented: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/keremp/opus-em-augmented/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://keremp/opus-em-augmented/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/keremp/opus-em-augmented/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 60b49fecfc9140804c6e1002216ecbb0ae04ffeccc6759039b21e1c46af00972
- Size of remote file:
- 438 MB
- SHA256:
- 16ab45defda402108df39a288f66351f47bef3f83b456905f024aa0e194de620
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