Instructions to use Sahajtomar/German_Zeroshot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sahajtomar/German_Zeroshot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Sahajtomar/German_Zeroshot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sahajtomar/German_Zeroshot") model = AutoModelForSequenceClassification.from_pretrained("Sahajtomar/German_Zeroshot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Sahajtomar/German_Zeroshot: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/Sahajtomar/German_Zeroshot/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Sahajtomar/German_Zeroshot/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Sahajtomar/German_Zeroshot/resolve/main/flax_model.msgpack
1.34 GB
- Xet hash:
- d5e4b2a458f10d1db43afcd4b6a8688f8b230e10040038228529ac985c83b263
- Size of remote file:
- 1.34 GB
- SHA256:
- a9ad69965585f02f44c4fb5b5d7a3cbfcd70223da99e608ce55c9e26252804de
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