Sentence Similarity
sentence-transformers
Safetensors
French
bert
feature-extraction
text-embeddings-inference
Instructions to use crazyjeannot/literary_bge_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use crazyjeannot/literary_bge_base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("crazyjeannot/literary_bge_base") 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] - Notebooks
- Google Colab
- Kaggle
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README.md
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This is an encoder model finetuned from the FlagOpen/FlagEmbedding family of models.
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The model is specialized for studying french literary fiction with a training
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It maps paragraphs to a 1024-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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This is an encoder model finetuned from the FlagOpen/FlagEmbedding family of models.
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The model is specialized for studying french literary fiction with a training corpus based on 40.000 passages from free from rights french literary novels.
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It maps paragraphs to a 1024-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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