rimoKR/github-issue-tag-classifier

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This is a fine-tuned bert-base-uncased model for multi-label classification of GitHub issues into various tags (e.g., bug, enhancement, documentation, etc.).

Model Details

• Base model: bert-base-uncased
• Task: Multi-label Text Classification
• Labels: 19 possible tags (e.g., bug, dataset request, help wanted, etc.)
• Tokenizer: bert-base-uncased

Model Description

This model performs multi-label classification of GitHub issues based on their content. Each issue is represented by a combination of its title, body, state, and associated comments. These components are concatenated into a single input string using the following format:

  if example.get("title"):
          text_parts.append("Title: " + example["title"])
      if example.get("body"):
          text_parts.append("Body: " + example["body"])
      if example.get("state"):
          text_parts.append("State: " + example["state"])
  
      comments = example.get("comments", [])
      if comments:
          text_parts.append("Comments: " + " ".join(comments))
  
      return {"text": " \n ".join(text_parts)}

The resulting "text" field serves as the input to the model. Each text entry is tokenized using the Hugging Face bert-base-uncased tokenizer with the following configuration:

  tokenizer(
        example["text"],
        padding="max_length",
        truncation=True,
        max_length=512
  )

The target labels are constructed as a binary vector of length 19, where each element corresponds to one of the predefined GitHub issue tags (e.g., bug, enhancement, documentation, etc.). Each element in the vector is set to 1 if the tag is present for the issue, and 0 otherwise. This format enables the model to perform multi-label classification, allowing it to assign multiple relevant tags to a single GitHub issue.

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How to Get Started with the Model

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Training Details

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Summary

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

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