models/model_cards/python/setfit/README.md

metadata

language:
  - en
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
license:
  - mit
datasets:
  - NLBSE/nlbse26-code-comment-classification
widget:
  - text: dataright np^sin 2 np^pi 224 t | Audio
  - text: >-
      robust way to ask the database for its current transaction state. |
      AtomicTests
  - text: the string marking the beginning of a print statement. | Environment
  - text: handled otherwise by a particular method. | StringMethods
  - text: table. | PlotAccessor
metrics:
  - 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 (Python)
          type: NLBSE/nlbse26-code-comment-classification
          split: test
        metrics:
          - type: accuracy
            value: 0.4482758620689655
            name: Accuracy

SetFit Model for Python Code Comment Classification

Model Details

• Model Type: SetFit (Sentence Transformer Fine-tuning)
• Base Model: sentence-transformers/paraphrase-MiniLM-L6-v2
• Language: Python (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 Python subset of the NLBSE Code Comment Classification Dataset. It is designed to classify code comments into categories that describe the semantic purpose of the comment (e.g., Summary, Usage, Parameters).

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 Python projects. As such, it is useful for research and development in code comment classification of projects made in Python, 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 Python code comments (including inline comments # and docstrings """).
• Comment Types: The model has been evaluated on the following categories specific to software documentation:
  1. Summary
  2. Usage
  3. Parameters
  4. Expand
  5. DevelopmentNotes

Metrics

• Model Performance Measures: The primary metrics used for evaluation are Precision, Recall, F1-Score, and Accuracy.
• Decision Thresholds: A probability threshold of 0.5 was used for classification.
• Global Performance: The model achieves an overall Accuracy of 0.4483 on the test set.

Evaluation Data

• Dataset: NLBSE Code Comment Classification Dataset (Python test split).
• Motivation: This dataset was chosen because it is the established benchmark for the NLBSE (Natural Language-Based Software Engineering) workshop.
• Size 290 rows.
• Preprocessing: Comments were extracted from real-world open-source Python projects, split into sentences, and manually classified.

Training Data

• Dataset: NLBSE Code Comment Classification Dataset (Python train split).
• Dataset Stats:

  Training set	Min	Median	Max
  Word count	3	15.5217	299

Quantitative Analyses

The following table shows the performance breakdown per category on the Python test set:

Language	Category	Precision	Recall	F1-Score
python	Summary	0.6897	0.6557	0.6723
python	Usage	0.6667	0.6813	0.6739
python	Parameters	0.6882	0.7529	0.7191
python	Expand	0.4533	0.6667	0.5397
python	DevelopmentNotes	0.2192	0.5000	0.3048

Ethical Considerations

• Biases: The dataset is drawn from open-source software projects. The comments reflect the writing styles and norms of the open-source Python community.
• Content: Comments are user-generated content and may contain informal language or jargon.

Caveats and Recommendations

• Performance Variation: The model performs well on structural comments like Parameters (F1 0.72) but struggles significantly with DevelopmentNotes (F1 0.30). 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:

pip install setfit

Then you can load this model and run inference:

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("se4ai2526-uniba/setfit-python") 

# Run inference
preds = model(["# yields the next value | generator.py"])
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

Training Results

Metric	Value
Accuracy	0.4482758620689655
Embedding Loss	0.177
Training Loss	0.215
Training Runtime	137.40 s
Training Samples/Sec	198.189
Training Steps/Sec	6.213

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 – Python Model." 2025.

BibTeX:

@misc{theclouds_nlbse26_code_comment_classification_python,
  title        = {NLBSE'26 Code Comment Classification: Python 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