Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
text-embeddings-inference
Instructions to use UMCU/BioLORD-2023-M-Dutch-InContext-v1-ST_bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use UMCU/BioLORD-2023-M-Dutch-InContext-v1-ST_bf16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("UMCU/BioLORD-2023-M-Dutch-InContext-v1-ST_bf16") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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# SentenceTransformer based on FremyCompany/BioLORD-2023-M-Dutch-InContext-v1
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This is a [sentence-transformers](https://www.SBERT.net)
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## Model Details
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# SentenceTransformer based on FremyCompany/BioLORD-2023-M-Dutch-InContext-v1
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This is a [sentence-transformers](https://www.SBERT.net) from, simply the [FremyCompany/BioLORD-2023-M-Dutch-InContext-v1](https://huggingface.co/FremyCompany/BioLORD-2023-M-Dutch-InContext-v1) model but with bf16 instead of float32. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
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## Model Details
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