Instructions to use rasgaard/m2v-dfm-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use rasgaard/m2v-dfm-large with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("rasgaard/m2v-dfm-large") - sentence-transformers
How to use rasgaard/m2v-dfm-large with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rasgaard/m2v-dfm-large") 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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## Installation
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Install model2vec using pip:
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## Scandinavian Embedding Benchmark
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## Installation
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Install model2vec using pip:
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