Instructions to use minishlab/M2V_base_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/M2V_base_output with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/M2V_base_output") - sentence-transformers
How to use minishlab/M2V_base_output with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/M2V_base_output") 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
Upload README.md with huggingface_hub
Browse files
README.md
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model_name: M2V_base_output
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base_model: BAAI/bge-base-en-v1.5
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language:
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license: mit
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# M2V_base_output Model Card
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base_model: BAAI/bge-base-en-v1.5
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language:
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- en
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library_name: model2vec
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license: mit
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model_name: M2V_base_output
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tags:
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- embeddings
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- sentence-transformers
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- static-embeddings
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# M2V_base_output Model Card
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