Instructions to use Sebb/german-nli-base-thesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebb/german-nli-base-thesis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sebb/german-nli-base-thesis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sebb/german-nli-base-thesis") model = AutoModelForSequenceClassification.from_pretrained("Sebb/german-nli-base-thesis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sebb/german-nli-base-thesis: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/Sebb/german-nli-base-thesis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sebb/german-nli-base-thesis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sebb/german-nli-base-thesis/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- 66a78424c281cab9fc6d1f4cbfe784147b0e55e91c4a04c7d7d78227b026030f
- Size of remote file:
- 440 MB
- SHA256:
- 4da7c3a332fe4e9560f7e3c5a8f85820f86d74de0b8312769589b71590b7a523
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