# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

Model Card: Bug Classification Algorithm

Purpose: To classify software bugs according to their clarity, relevance, and readability using a revamped dataset of historical bugs.

Model Type: Machine Learning Model (Supervised Learning)

Dataset Information:

Historical Software Bugs Dataset
Split into training and validation sets - Training Data consists of approximately 80% of data and validation/testing data comprises of the remaining 20%.
Each example contains features including descriptions of software bugs along with human annotations specifying whether they were clear, relevant, and readable.
Features Extracted:
- 1. Text description of the bug
- 2. Number of lines of code affected by the bug
- 3. Timestamp of bug submission
- 4. Version control tags associated with the bug
- 5. Priority level assigned to the bug
- 6. Type of software component impacted by the bug
- 7. Operating system compatibility of the software
- 8. Programming language used to develop the software
- 9. Hardware specifications required to run the software

Models Trained:

Naive Bayes Classifier
Random Forest Classifier
Gradient Boosting Classifier
Neural Networks with Convolutional Layers
Hyperparameter tuning techniques: Cross-validation, Grid Search and Random Search applied to each model architecture.

Metrics Used For Evaluation:

Accuracy Score: Fraction of correctly predicted examples out of total examples.
Precision: Ratio of correct positive predictions over all positive predictions made by the model.
Recall: Ratio of true positives found among actual positives.
F1 score: Harmonic mean of precision and recall indicating
{"language": ["en"], "tags": ["text-classification"], "widget": [{"text": "The app crashed when I opened it this morning. Can you fix this please?", "example_title": "Likely bug report"}, {"text": "Please add a like button!", "example_title": "Unlikely bug report"}]}
transformers distilbert text-classification en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us 

# Model Card for Model ID



This modelcard aims to be a base template for new models. It has been generated using this raw template.

## Model Details

### Model Description

- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: 

Model Card: Bug Classification Algorithm

Purpose: To classify software bugs according to their clarity, relevance, and readability using a revamped dataset of historical bugs.

Model Type: Machine Learning Model (Supervised Learning)

Dataset Information:

Historical Software Bugs Dataset
Split into training and validation sets - Training Data consists of approximately 80% of data and validation/testing data comprises of the remaining 20%.
Each example contains features including descriptions of software bugs along with human annotations specifying whether they were clear, relevant, and readable.
Features Extracted:
- 1. Text description of the bug
- 2. Number of lines of code affected by the bug
- 3. Timestamp of bug submission
- 4. Version control tags associated with the bug
- 5. Priority level assigned to the bug
- 6. Type of software component impacted by the bug
- 7. Operating system compatibility of the software
- 8. Programming language used to develop the software
- 9. Hardware specifications required to run the software

Models Trained:

Naive Bayes Classifier
Random Forest Classifier
Gradient Boosting Classifier
Neural Networks with Convolutional Layers
Hyperparameter tuning techniques: Cross-validation, Grid Search and Random Search applied to each model architecture.

Metrics Used For Evaluation:

Accuracy Score: Fraction of correctly predicted examples out of total examples.
Precision: Ratio of correct positive predictions over all positive predictions made by the model.
Recall: Ratio of true positives found among actual positives.
F1 score: Harmonic mean of precision and recall indicating
# Model Card for Model ID



This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description

- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: 

Model Card: Bug Classification Algorithm

Purpose: To classify software bugs according to their clarity, relevance, and readability using a revamped dataset of historical bugs.

Model Type: Machine Learning Model (Supervised Learning)

Dataset Information:

Historical Software Bugs Dataset
Split into training and validation sets - Training Data consists of approximately 80% of data and validation/testing data comprises of the remaining 20%.
Each example contains features including descriptions of software bugs along with human annotations specifying whether they were clear, relevant, and readable.
Features Extracted:
- 1. Text description of the bug
- 2. Number of lines of code affected by the bug
- 3. Timestamp of bug submission
- 4. Version control tags associated with the bug
- 5. Priority level assigned to the bug
- 6. Type of software component impacted by the bug
- 7. Operating system compatibility of the software
- 8. Programming language used to develop the software
- 9. Hardware specifications required to run the software

Models Trained:

Naive Bayes Classifier
Random Forest Classifier
Gradient Boosting Classifier
Neural Networks with Convolutional Layers
Hyperparameter tuning techniques: Cross-validation, Grid Search and Random Search applied to each model architecture.

Metrics Used For Evaluation:

Accuracy Score: Fraction of correctly predicted examples out of total examples.
Precision: Ratio of correct positive predictions over all positive predictions made by the model.
Recall: Ratio of true positives found among actual positives.
F1 score: Harmonic mean of precision and recall indicating
TAGS
#transformers #distilbert #text-classification #en #autotrain_compatible #endpoints_compatible #region-us 
 # Model Card for Model ID



This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description

- Developed by: 
- Shared by [optional]: 
- Model type: 
- Language(s) (NLP): 
- License: 
- Finetuned from model [optional]: 

Model Card: Bug Classification Algorithm

Purpose: To classify software bugs according to their clarity, relevance, and readability using a revamped dataset of historical bugs.

Model Type: Machine Learning Model (Supervised Learning)

Dataset Information:

Historical Software Bugs Dataset
Split into training and validation sets - Training Data consists of approximately 80% of data and validation/testing data comprises of the remaining 20%.
Each example contains features including descriptions of software bugs along with human annotations specifying whether they were clear, relevant, and readable.
Features Extracted:
- 1. Text description of the bug
- 2. Number of lines of code affected by the bug
- 3. Timestamp of bug submission
- 4. Version control tags associated with the bug
- 5. Priority level assigned to the bug
- 6. Type of software component impacted by the bug
- 7. Operating system compatibility of the software
- 8. Programming language used to develop the software
- 9. Hardware specifications required to run the software

Models Trained:

Naive Bayes Classifier
Random Forest Classifier
Gradient Boosting Classifier
Neural Networks with Convolutional Layers
Hyperparameter tuning techniques: Cross-validation, Grid Search and Random Search applied to each model architecture.

Metrics Used For Evaluation:

Accuracy Score: Fraction of correctly predicted examples out of total examples.
Precision: Ratio of correct positive predictions over all positive predictions made by the model.
Recall: Ratio of true positives found among actual positives.
F1 score: Harmonic mean of precision and recall indicating