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--- |
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library_name: transformers |
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tags: |
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- github |
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datasets: |
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- lewtun/github-issues |
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language: |
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- en |
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base_model: |
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- google-bert/bert-base-uncased |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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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.). |
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## Model Details |
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- **Base model**: [bert-base-uncased](https://huggingface.co/bert-base-uncased) |
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- **Task**: Multi-label Text Classification |
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- **Labels**: 19 possible tags (e.g., `bug`, `dataset request`, `help wanted`, etc.) |
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- **Tokenizer**: `bert-base-uncased` |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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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: |
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```python |
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if example.get("title"): |
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text_parts.append("Title: " + example["title"]) |
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if example.get("body"): |
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text_parts.append("Body: " + example["body"]) |
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if example.get("state"): |
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text_parts.append("State: " + example["state"]) |
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comments = example.get("comments", []) |
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if comments: |
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text_parts.append("Comments: " + " ".join(comments)) |
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return {"text": " \n ".join(text_parts)} |
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``` |
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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: |
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```python |
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tokenizer( |
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example["text"], |
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padding="max_length", |
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truncation=True, |
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max_length=512 |
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) |
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``` |
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The target labels are constructed as a binary vector of length 19, where each element corresponds to one of the predefined GitHub |
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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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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
