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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
widget:
  - text: 'def print_hello_world():'
    example_title: Hello world
    group: Python
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

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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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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Technical Specifications [optional]

Model Architecture and Objective

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Hardware

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Software

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