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sm-riti16
/
course-skill-bi-encoder

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:14131
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use sm-riti16/course-skill-bi-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use sm-riti16/course-skill-bi-encoder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("sm-riti16/course-skill-bi-encoder")
    
    sentences = [
        "Honors Thesis I. Business students with outstanding academic records may undertake an Honors Thesis. The topic is of the student's choice but must have some original aspect in the question being explored, the data set, or in the methods that are used. It must also be of sufficient academic rigor to meet the approval of a faculty advisor with expertise in the project's area. Students enroll each semester in a 9-unit independent study course with their faculty advisor for the project (70-500 in the fall and 70-501 in the spring). Students and their faculty advisor develop a course description for the project and submit it for approval as two 9-unit courses to the BA department. Enrollment by permission of the BA Program. Industry: business & management. Level: advanced.",
        "project management",
        "statistics",
        "natural language processing"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
course-skill-bi-encoder
92.1 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
sm-riti16's picture
sm-riti16
Update README.md
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  • .gitattributes
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  • README.md
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  • config.json
    636 Bytes
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  • config_sentence_transformers.json
    294 Bytes
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  • model.safetensors
    90.9 MB
    xet
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  • modules.json
    368 Bytes
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  • sentence_bert_config.json
    60 Bytes
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  • skill_emb_trained.npy
    247 kB
    xet
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  • skills_index.csv
    4.3 kB
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  • special_tokens_map.json
    732 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    1.53 kB
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  • vocab.txt
    232 kB
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