Feature Extraction
Transformers
Safetensors
English
deberta-v2
embedding
scientific
abstract
text-embeddings-inference
Instructions to use CLAUSE-Bielefeld/SemCSE_cosine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLAUSE-Bielefeld/SemCSE_cosine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CLAUSE-Bielefeld/SemCSE_cosine")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("CLAUSE-Bielefeld/SemCSE_cosine") model = AutoModel.from_pretrained("CLAUSE-Bielefeld/SemCSE_cosine") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ceef70ee5c068f2e8ec2ea787d1566d2f5846b612f0e0a3879edffa246dc91cf
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
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