Instructions to use minishlab/potion-retrieval-32M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/potion-retrieval-32M with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/potion-retrieval-32M") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - sentence-transformers
How to use minishlab/potion-retrieval-32M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/potion-retrieval-32M") 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
Add training dataset metadata
Browse files
README.md
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library_name: model2vec
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license: mit
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model_name: potion-retrieval-32M
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---
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datasets:
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- sentence-transformers/gooaq
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- sentence-transformers/msmarco-co-condenser-margin-mse-sym-mnrl-mean-v1
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- sentence-transformers/squad
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- sentence-transformers/s2orc
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- sentence-transformers/all-nli
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- sentence-transformers/paq
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- sentence-transformers/trivia-qa
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- bclavie/msmarco-10m-triplets
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- nthakur/swim-ir-monolingual
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- sentence-transformers/pubmedqa
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- sentence-transformers/miracl
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- sentence-transformers/mldr
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- sentence-transformers/mr-tydi
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library_name: model2vec
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license: mit
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model_name: potion-retrieval-32M
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