Instructions to use malteos/scincl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use malteos/scincl with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("malteos/scincl") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use malteos/scincl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="malteos/scincl")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("malteos/scincl") model = AutoModel.from_pretrained("malteos/scincl", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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Add tags/library_name for tighter integration with HF
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (c1beb05c991383fade07abb2032028748941e9a7) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>5cab047
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Upload vocab.txt
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Upload train_triples.csv.gz with git-lfs
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