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Teradata
/
codesage-small-v2

Feature Extraction
Transformers
ONNX
code
teradata
byom
embeddings
custom_code
Model card Files Files and versions
xet
Community

Instructions to use Teradata/codesage-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Teradata/codesage-small-v2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="Teradata/codesage-small-v2", trust_remote_code=True)
    # Load model directly
    from transformers import CodeSage
    model = CodeSage.from_pretrained("Teradata/codesage-small-v2", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
codesage-small-v2
645 MB
Ctrl+K
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  • 1 contributor
History: 2 commits
sasha-smirnov's picture
sasha-smirnov
Initial publish via td-embeddings
31b8384 verified about 18 hours ago
  • onnx
    Initial publish via td-embeddings about 18 hours ago
  • .gitattributes
    1.52 kB
    initial commit about 18 hours ago
  • README.md
    7.67 kB
    Initial publish via td-embeddings about 18 hours ago
  • config.json
    863 Bytes
    Initial publish via td-embeddings about 18 hours ago
  • special_tokens_map.json
    582 Bytes
    Initial publish via td-embeddings about 18 hours ago
  • tokenizer.json
    3.48 MB
    Initial publish via td-embeddings about 18 hours ago
  • tokenizer_config.json
    798 Bytes
    Initial publish via td-embeddings about 18 hours ago