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apsys
/
minang-embedder

Sentence Similarity
sentence-transformers
Safetensors
Minangkabau
Indonesian
English
eurobert
feature-extraction
text-embeddings
retrieval
minangkabau
minang
indonesian
nusax
low-resource
custom_code
Model card Files Files and versions
xet
Community

Instructions to use apsys/minang-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use apsys/minang-embedder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("apsys/minang-embedder", trust_remote_code=True)
    
    sentences = [
        "Paliang suko bana makan siang di siko ayam jo ladonyo lamak bana harago lua biaso himat.",
        "I love having lunch here because the chicken and sambal are delicious and inexpensive.",
        "Kurang pas kalau bakunjuang ka banduang tanpa mancicipi batagor.",
        "The train ticket was booked yesterday."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
minang-embedder
441 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
apsys's picture
apsys
Upload Minang embedder model artifact
b9cbf7d verified 6 days ago
  • 1_Pooling
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  • .gitattributes
    1.57 kB
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  • LICENSE.md
    509 Bytes
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  • README.md
    7.49 kB
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  • config.json
    1.36 kB
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  • config_sentence_transformers.json
    300 Bytes
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  • configuration_eurobert.py
    12.1 kB
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  • model.safetensors
    424 MB
    xet
    Upload Minang embedder model artifact 6 days ago
  • modeling_eurobert.py
    49 kB
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  • modules.json
    429 Bytes
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  • sentence_bert_config.json
    241 Bytes
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  • tokenizer.json
    17.2 MB
    xet
    Upload Minang embedder model artifact 6 days ago
  • tokenizer_config.json
    486 Bytes
    Upload Minang embedder model artifact 6 days ago