models/model_cards/pharo/setfit/README.md

---
language:
- en
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
license:
- mit
datasets:
- NLBSE/nlbse26-code-comment-classification
metrics:
- f1
- precision
- recall
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: false
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 (Pharo)
      type: NLBSE/nlbse26-code-comment-classification
      split: test
    metrics:
    - type: accuracy
      value: 0.5673
      name: Accuracy
---

# SetFit Model for Pharo 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:** Pharo (Comments in English)
- **License:** MIT
- **Developed by:** TheClouds
- **Model Date:** November 17, 2025
- **Model Version:** 1.0
- **Maximum Sequence Length:** 128 tokens
- **Contact:** For questions or comments about this model, please contact us via GitHub or email.

### Description
This model is a SetFit model trained on the **Pharo** subset of the **NLBSE Code Comment Classification Dataset**. It is designed to classify code comments into one or more of **6 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 Pharo projects. As such, it is useful for research and development in code comment classification of projects made in Pharo, or software documentation analysis tasks.

### Out-of-Scope Use Cases
General text classification outside the domain of software engineering (e.g., social media sentiment analysis) is out of scope.

## Factors

- **Programming Language:** The model is specifically trained on Pharo code comments.
- **Comment Types:** The model recognizes the following 6 categories specific to Pharo documentation:
    1. `Keyimplementationpoints`
    2. `Example`
    3. `Responsibilities`
    4. `Intent`
    5. `Keymessages`
    6. `Collaborators`

## Metrics

- **Model Performance Measures:** The primary metrics used for evaluation are **Precision**, **Recall**, and **F1-Score**.
- **Performance:** The model achieves an average F1-Score of 0.4628 on the test set.

## Evaluation Data

- **Dataset:** NLBSE Code Comment Classification Dataset (Pharo test split).
- **Size:** 208 rows.
- **Preprocessing:** Comments were extracted from real-world open-source Pharo projects, split into sentences, and manually classified.

## Training Data

- **Dataset:** NLBSE Code Comment Classification Dataset (Pharo train split).
- **Size:** 900 rows.
- **Label Distribution:** The dataset contains 6 categories with varying frequencies. 

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

- **Pharo Labels (6):** `collaborators`, `example`, `intent`, `keyimplementationpoints`, `keymessages`, `responsibilities`.

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

## Quantitative Analyses
The following table shows the performance breakdown per category on the Pharo test set:

| lan   | cat                         | precision | recall   | f1       |
| ----- | --------------------------- | --------- | -------- | -------- |
| pharo | **Keyimplementationpoints** | 0.562500  | 0.642857 | 0.600000 |
| pharo | **Example**                 | 0.886364  | 0.876404 | 0.881356 |
| pharo | **Responsibilities**        | 0.632653  | 0.738095 | 0.681319 |
| pharo | **Intent**                  | 0.720000  | 0.857143 | 0.782609 |
| pharo | **Keymessages**             | 0.478261  | 0.733333 | 0.578947 |
| pharo | **Collaborators**           | 0.103448  | 0.428571 | 0.166667 |

## 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

- **Performance Variation:** The model performs well on `example` comments (F1 0.881) and `intent` comments (F1 0.782) but struggles significantly with all the other categories. Users should exercise caution when relying on the model for identifying development notes or rationale.
- **Context:** The model relies on text-only comment sentences. Surrounding code context is not included.

## 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-pharo") # Replace with actual model ID if different

# Run inference
preds = model(["each phase knows about its start time and send a corresponding event once the phase is completed. | BlSpaceFramePhase"])
print(preds)
```

## Training Details

### Training Hyperparameters

- batch_size: (32, 32)
- body_learning_rate: (2e-05, 1e-05)
- distance_metric: cosine_distance
- end_to_end: False
- eval_delay: False
- eval_max_steps: -1
- eval_steps: None
- eval_strategy: IntervalStrategy.NO
- evaluation_strategy: None
- greater_is_better: False
- head_learning_rate: 0.01
- l2_weight: 0.01
- load_best_model_at_end: False
- loss: CosineSimilarityLoss
- margin: 0.25
- max_length: None
- max_steps: -1
- metric_for_best_model: embedding_loss
- num_epochs: (2, 2)
- num_iterations: 5
- samples_per_label: 2
- sampling_strategy: oversampling
- save_steps: 500
- save_strategy: steps
- save_total_limit: 1
- seed: 42
- use_amp: False
- warmup_proportion: 0.1

### Training Results

| Metric                   | Value      |
| :----------------------- | :--------- |
| **Accuracy**             | 0.5673     |
| **Embedding Loss**       | 0.105      |
| **Training Loss**        | 0.1566     |
| **Training Runtime**     | 161.2121 s |
| **Training Samples/Sec** | 111.654    |
| **Training Steps/Sec**   | 3.498      |

### 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 – Pharo Model." 2025.

BibTeX:

```bibtex
@misc{theclouds_nlbse26_code_comment_classification_pharo,
  title        = {NLBSE'26 Code Comment Classification: Pharo 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

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