Instructions to use Sebb/german-nli-large-thesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sebb/german-nli-large-thesis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Sebb/german-nli-large-thesis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Sebb/german-nli-large-thesis") model = AutoModelForSequenceClassification.from_pretrained("Sebb/german-nli-large-thesis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Sebb/german-nli-large-thesis: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/Sebb/german-nli-large-thesis/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Sebb/german-nli-large-thesis/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Sebb/german-nli-large-thesis/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- d7a4f1c0f29236212b4eaa7b7bb9d4e56667bb32b2e410e9fbcf86fb2a8bd78b
- Size of remote file:
- 1.34 GB
- SHA256:
- 76f1e1d53f319af204b4e559c9959638a09c536cd3f2bcf95872e261988036c9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.