Feature Extraction
Transformers
Safetensors
English
deberta-v2
embedding
scientific
abstract
text-embeddings-inference
Instructions to use CLAUSE-Bielefeld/SemCSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CLAUSE-Bielefeld/SemCSE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CLAUSE-Bielefeld/SemCSE")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("CLAUSE-Bielefeld/SemCSE") model = AutoModel.from_pretrained("CLAUSE-Bielefeld/SemCSE") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b5c2ff10f2ef57826ce8771f64d8cc5150555105d08c7d559fa4cc15520a5a2
- Size of remote file:
- 735 MB
- SHA256:
- 77ac6a2a1985b95e274a0666cac04045e384a86cc0471e53824681e3fdc68808
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