library_name: transformers
tags:
- github
datasets:
- lewtun/github-issues
language:
- en
base_model:
- google-bert/bert-base-uncased
Model Card for Model ID
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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