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lambdasec
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santafixer

Text Generation
Transformers
PyTorch
code
gpt2
custom_code
Eval Results
text-generation-inference
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metadata
license: apache-2.0
datasets:
  - lambdasec/cve-single-line-fixes
  - lambdasec/gh-top-1000-projects-vulns
language:
  - code
tags:
  - code
programming_language:
  - Java
  - JavaScript
  - Python
inference: false
model-index:
  - name: SantaFixer
    results:
      - task:
          type: text-generation
        dataset:
          type: lambdasec/gh-top-1000-projects-vulns
          name: GH Top 1000 Projects Vulnerabilities
        metrics:
          - name: pass@10 (Java)
            type: pass@10
            value: 0.1
            verified: false
          - name: pass@10 (Python)
            type: pass@10
            value: 0.2
            verified: false
          - name: pass@10 (JavaScript)
            type: pass@10
            value: 0.3
            verified: false

Model Card for SantaFixer

This is a LLM for code that is focussed on generating bug fixes using infilling.

Model Details

Model Description

Model Sources [optional]

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Uses

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Evaluation

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Metrics

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Results

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Model Architecture and Objective

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