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

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
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](https://huggingface.co/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

<!-- Provide a longer summary of what this model is. -->

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:
```python
  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:
```python
  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.



### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses

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### 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

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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

Use the code below to get started with the model.


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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### 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

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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