Instructions to use cl-trier/gbert-base_sosec-relevance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cl-trier/gbert-base_sosec-relevance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="cl-trier/gbert-base_sosec-relevance")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("cl-trier/gbert-base_sosec-relevance") model = AutoModelForSequenceClassification.from_pretrained("cl-trier/gbert-base_sosec-relevance", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Department of Computational Linguistics - University of Trier
Upload BertForSequenceClassification
2fa4ff6 Download pytorch_model.bin from cl-trier/gbert-base_sosec-relevance: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/cl-trier/gbert-base_sosec-relevance/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cl-trier/gbert-base_sosec-relevance/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cl-trier/gbert-base_sosec-relevance/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- baeedce3d1d820a0135d639274542710e5b13f1753c2b8d3be9303e09385c9c8
- Size of remote file:
- 440 MB
- SHA256:
- 869310ef912e63c78f7dc17dfe722bba8a34ee1b77f116a0af2f31c6350c13e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.