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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("malteos/scincl") model = AutoModel.from_pretrained("malteos/scincl") - Inference
- Notebooks
- Google Colab
- Kaggle
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- accuracy
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---
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## SciNCL
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Additional evaluations are available in the paper.
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- accuracy
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license: mit
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---
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## SciNCL
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Additional evaluations are available in the paper.
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## License
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MIT
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