Instructions to use minishlab/potion-base-8M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/potion-base-8M with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/potion-base-8M") - sentence-transformers
How to use minishlab/potion-base-8M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/potion-base-8M") 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
Update config.json
Browse files- config.json +1 -1
config.json
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{"tokenizer_name": "baai/bge-base-en-v1.5", "apply_pca": 256, "apply_zipf": true, "hidden_dim": 256, "seq_length": 1000000, "normalize": true}
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{"model_type": "model2vec", "architectures": ["StaticModel"],"tokenizer_name": "baai/bge-base-en-v1.5", "apply_pca": 256, "apply_zipf": true, "hidden_dim": 256, "seq_length": 1000000, "normalize": true}
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