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sobamchan
/
monnem-large-v0

Sentence Similarity
sentence-transformers
Safetensors
modernbert
feature-extraction
Generated from Trainer
dataset_size:8963241
loss:EmbedDistillLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use sobamchan/monnem-large-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use sobamchan/monnem-large-v0 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("sobamchan/monnem-large-v0")
    
    sentences = [
        "A man in shorts and a woman in a black and white polka dot bikini sunbathing on the beach.",
        "A man is standing in a field with a green plant in it.",
        "A woman preparing some pork.",
        "uh-huh uh-huh well what other movies have you seen then lately"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
monnem-large-v0
1.58 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
sobamchan's picture
sobamchan
Upload folder using huggingface_hub
8e4d28a verified about 18 hours ago
  • 1_Pooling
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  • .gitattributes
    1.52 kB
    initial commit about 18 hours ago
  • README.md
    5.82 kB
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  • config.json
    1.21 kB
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  • config_sentence_transformers.json
    284 Bytes
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  • model.safetensors
    1.58 GB
    xet
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  • modules.json
    277 Bytes
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  • sentence_bert_config.json
    241 Bytes
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  • special_tokens_map.json
    694 Bytes
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  • tokenizer.json
    3.58 MB
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  • tokenizer_config.json
    20.8 kB
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