models/model_cards/java/setfit/README.md

---
language:
- en
tags:
- text-classification
- code-comment-classification
- setfit
- java
- software-engineering
- multi-label
- sentence-transformers
- generated_from_setfit_trainer
license: mit
datasets:
- NLBSE/nlbse26-code-comment-classification
metrics:
- f1
- precision
- recall
pipeline_tag: text-classification
library_name: setfit
inference: false
widget:
- text: '@link FSNamesystem#readLock() | FSPermissionChecker.java'
- text: previous^checkpoint li | TestSaveNamespace.java
- text: // the file doesn't have anything | TaskLog.java
- text: " @param file the file the include directives point to\n\t * @param depth\
    \ depth to which includes are followed, should be one of\n\t * {@link #DEPTH_ZERO}\
    \ or {@link #DEPTH_INFINITE}\n\t * @return an array of include relations\n\t *\
    \ @throws CoreException | IIndex.java"
- text: // quotes are removed | ScannerUtility.java
base_model: sentence-transformers/paraphrase-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-MiniLM-L6-v2
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: NLBSE Code Comment Classification Dataset (Java)
      type: NLBSE/nlbse26-code-comment-classification
      split: test
    metrics:
    - type: accuracy
      value: 0.7435
      name: Accuracy
---

# SetFit Model for Java Code Comment Classification

## Model Details

- **Model Type:** SetFit (Sentence Transformer Fine-tuning)
- **Base Model:** [sentence-transformers/paraphrase-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L6-v2)
- **Language:** Java (Comments in English)
- **License:** MIT
- **Developed by:** TheClouds
- **Model Date:** November 4, 2025
- **Model Version:** 1.0
- **Contact:** For additional information contact team TheClouds on github


### Description
This model is a SetFit model trained on the **Java** subset of the **NLBSE Code Comment Classification Dataset**. It is designed to classify code comments into one or more of **7 categories** that describe the semantic purpose of the comment.

The model uses a multi-label classification approach, where a single comment can belong to multiple categories.

## Intended Use
This model has been created for the Code Comment Classification task, and trained specifically on code comments extracted from Java projects. As such, it is useful for research and development in code comment classification of projects made in Java or other object-oriented languages, or software documentation analysis tasks that perform supervised multi-label classification.

### Out-of-Scope Use Cases
General text classification outside the domain of software engineering.

## Factors

- **Programming Language:** The model is specifically trained on Java code comments.
- **Comment Types:** The model recognizes the following 7 categories specific to Java documentation:
    1. `summary`
    2. `Ownership`
    3. `Expand`
    4. `usage`
    5. `Pointer`
    6. `deprecation`
    7. `rational`

## Metrics

- **Model Performance Measures:** The primary metrics used for evaluation are **Precision**, **Recall**, **F1-Score** and **Accuracy**.
- **Decision Threshold:** A probability threshold of 0.5 was used for classification.
- **Performance:** The model achieves an overall Accuracy of **0.7669** on the test set.

### Dataset Summary
The **NLBSE Code Comment Classification Dataset** is a collection of code comment sentences accompanied by multi-label category annotations. 

- **Java Labels (7):** `summary`, `Ownership`, `Expand`, `usage`, `Pointer`, `deprecation`, `rational`.

Each entry corresponds to a comment sentence extracted from real projects.

### Motivation 
The usage of this specific dataset was a requirement of the NLBSE 2025 code comment classification challenge.

## Training Data

- **Dataset:** NLBSE Code Comment Classification Dataset (Java train split).
- **Size:** 5,390 rows.
- **Label Distribution:** The dataset contains 7 categories with varying frequencies. Common categories include "summary" and "usage".

## Evaluation Data

- **Dataset:** NLBSE Code Comment Classification Dataset (Java test split).
- **Size:** 1,200 rows.
- **Preprocessing:** Comments were extracted from real-world open-source Java projects, split into sentences, and manually classified.

## Quantitative Analyses

| lan | cat | precision | recall | f1 |
|---|---|---|---|---|
| java | summary | 0.871224 | 0.886731 | 0.878909 |
| java | Ownership | 1.000000 | 1.000000 | 1.000000 |
| java | Expand | 0.330097 | 0.430380 | 0.373626 |
| java | usage | 0.883803 | 0.850847 | 0.867012 |
| java | Pointer | 0.775641 | 0.968000 | 0.861210 |
| java | deprecation | 0.875000 | 0.700000 | 0.777778 |
| java | rational | 0.311688 | 0.413793 | 0.355556 |

## Ethical Considerations

- **Biases:** The dataset is drawn from open-source software projects. The comments reflect the writing styles and norms of the open-source community, which may not be representative of all software development environments (e.g., proprietary software).
- **Content:** Comments are user-generated content and may contain informal language or jargon specific to the projects they were extracted from.

## Caveats and Recommendations

- **Language Specificity:** The label set is specific to Java. 
- **Context:** The model relies on text-only comment sentences. Surrounding code context is not included, which may limit the model's ability to resolve ambiguous comments.
- **Class Imbalance:** Some categories (e.g., `deprecation`, `Ownership`) may be underrepresented compared to `summary` or `usage`.

## How to Use

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference:

```python
from setfit import SetFitModel

# Download from the 🤗 Huggingface Hub
model = SetFitModel.from_pretrained("se4ai2526-uniba/setfit-java") # Replace with actual model ID if different

# Run inference
preds = model(["// quotes are removed | ScannerUtility.java"])
print(preds)
```

## Training Details

### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (2, 2)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 5
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
- probability_threshold: 0.5

### Training Results
| Metric | Value |
|:-------|:------|
| **Accuracy** | 0.7669 |
| **Embedding Loss** | 0.0239 |
| **Training Loss** | 0.0587 |
| **Training Runtime** | 1515.40 s |
| **Training Samples/Sec** | 71.189 |
| **Training Steps/Sec** | 2.225 |

### Framework Versions
- Python: 3.11.9
- SetFit: 1.1.2
- Sentence Transformers: 5.1.2
- Transformers: 4.57.1
- PyTorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.22.1

## Citation

If you use this model in academic work or derived systems, please cite:

> TheClouds Team. "NLBSE'26 Code Comment Classification – Java Model." 2025.

BibTeX:

```bibtex
@misc{theclouds_nlbse26_code_comment_classification_java,
  title        = {NLBSE'26 Code Comment Classification: Java Model},
  author       = {TheClouds Team},
  year         = {2025},
  note         = {Model available on Hugging Face},
  howpublished = {\url{To be published}}
}
```

Contact:

For questions, feedback, or collaboration requests related to this model, please contact:
> Giacomo Signorile: g.signorile14@studenti.uniba.it
> Davide Pio Posa: d.posa3@studenti.uniba.it
> Marco Lillo: m.lillo21@studenti.uniba.it
> Rebecca Margiotta: m.margiotta5@studenti.uniba.it
> Adriano Gentile: a.gentile97@studenti.uniba.com

Issue tracker: https://github.com/se4ai2526-uniba/TheClouds


## Acknowledgements

This model was created for research in the context of **NLBSE (Natural Language-Based Software Engineering)**.