Model2Vec
Safetensors
sentence-transformers
English
embeddings
static-embeddings
mteb
Eval Results (legacy)
Instructions to use minishlab/M2V_base_glove with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use minishlab/M2V_base_glove with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("minishlab/M2V_base_glove") - sentence-transformers
How to use minishlab/M2V_base_glove with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("minishlab/M2V_base_glove") 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 +2 -0
config.json
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{
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"tokenizer_name": "word_level",
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"apply_pca": 256,
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"apply_zipf": true,
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{
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"model_type": "model2vec",
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"architectures": ["StaticModel"],
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"tokenizer_name": "word_level",
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"apply_pca": 256,
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"apply_zipf": true,
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