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victormuryn
/
use-stochastic-pt

Sentence Similarity
sentence-transformers
Safetensors
Transformers
xlm-roberta
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use victormuryn/use-stochastic-pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use victormuryn/use-stochastic-pt with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("victormuryn/use-stochastic-pt")
    
    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]
  • Transformers

    How to use victormuryn/use-stochastic-pt with Transformers:

    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("victormuryn/use-stochastic-pt")
    model = AutoModel.from_pretrained("victormuryn/use-stochastic-pt", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
use-stochastic-pt
1.13 GB
Ctrl+K
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  • 1 contributor
History: 5 commits
victormuryn's picture
victormuryn
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  • .gitattributes
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  • README.md
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  • config.json
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  • model.safetensors
    1.11 GB
    xet
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  • tokenizer.json
    17.1 MB
    xet
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  • tokenizer_config.json
    575 Bytes
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