Feature Extraction
Transformers
Safetensors
German
xlm-roberta
entity-linking
wikidata
umls
text-embeddings-inference
Instructions to use permediq/SapBERT-DE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use permediq/SapBERT-DE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="permediq/SapBERT-DE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("permediq/SapBERT-DE") model = AutoModel.from_pretrained("permediq/SapBERT-DE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from permediq/SapBERT-DE: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/permediq/SapBERT-DE/resolve/main/tokenizer.json
- Command line
-
hf download hf://permediq/SapBERT-DE/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/permediq/SapBERT-DE/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 0f3462c8eb77b328b39134bb358b451a9518f337da41e4b75a60ddb696c6b6f0
- Size of remote file:
- 17.1 MB
- SHA256:
- e8429013d0937d3ba3f2db2c9d652a2354cc6794491f72a96f18675639edacb9
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