AfroLogicInsect/topic-model-analysis-model

Model Card for Topic Classification Model

A fine-tuned DistilBERT model for multi-class topic classification. This model predicts the most relevant topic label from a predefined set based on input text. It was trained using 🤗 Transformers and PyTorch on a custom dataset derived from academic and news-style corpora.

Model Details

Model Description

This model was developed by Daniel (@AfroLogicInsect) to classify text into one of several predefined topics. It builds on the distilbert-base-uncased architecture and was fine-tuned for multi-class classification using a softmax output layer.

• Developed by: Daniel 🇳🇬 (@AfroLogicInsect)
• Model type: DistilBERT-based multi-class sequence classifier
• Language(s): English
• License: MIT
• Finetuned from: distilbert-base-uncased

Model Sources

• Repository: AfroLogicInsect/topic-model-analysis-model
• Paper: arXiv:1910.09700 (DistilBERT)
• Demo: [Coming soon]

Uses

Direct Use

• Classify academic or news-style text into topics such as AI, finance, sports, climate, etc.
• Embed in dashboards or content moderation tools for automatic tagging

Downstream Use

• Can be extended to hierarchical topic classification
• Useful for building recommendation engines or content filters

Out-of-Scope Use

• Not suitable for sentiment or emotion classification
• May not generalize well to informal or slang-heavy text

Bias, Risks, and Limitations

• Trained on curated corpora — may reflect biases in source material
• Topics are predefined and static — emerging topics may be misclassified
• Confidence scores are probabilistic, not definitive

Recommendations

• Use top_k=5 with return_all_scores=True to retrieve multiple topic predictions
• Consider fine-tuning on domain-specific data for improved accuracy

How to Get Started

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="AfroLogicInsect/topic-model-analysis-model",
    tokenizer="AfroLogicInsect/topic-model-analysis-model",
    return_all_scores=True
)

text = "New AI breakthrough in natural language processing"
results = classifier(text)
top_5 = sorted(results[0], key=lambda x: x['score'], reverse=True)[:5]
for i, res in enumerate(top_5):
    print(f"Top {i+1}: {res['label']} ({res['score']:.3f})")

Training Details

Dataset

• Custom multi-class topic dataset based on arXiv abstracts and news articles
• Labels include domains like AI, finance, sports, climate, etc.

Hyperparameters

• Epochs: 3
• Batch size: 16
• Learning rate: 2e-5
• Evaluation every 200 steps
• Metric: F1 score

Trainer Setup

Used Hugging Face Trainer API with TrainingArguments configured for early stopping and best model selection.

Evaluation

Model achieved strong performance across multiple topic categories. Evaluation metrics include:

• Accuracy: ~90.8%
• F1 Score: ~0.91
• Precision: ~0.89
• Recall: ~0.93

Environmental Impact

• Hardware: Google Colab (NVIDIA T4 GPU)
• Training Time: ~2.5 hours
• Carbon Emitted: ~0.3 kg CO₂eq (estimated via ML Impact Calculator)

Citation

@misc{afrologicinsect2025topicmodel,
  title = {AfroLogicInsect Topic Classification Model},
  author = {Akan Daniel},
  year = {2025},
  howpublished = {\url{https://huggingface.co/AfroLogicInsect/topic-model-analysis-model}},
}

Contact

• Name: Daniel (@AfroLogicInsect)
• Location: Lagos, Nigeria
• Contact: GitHub / Hugging Face / email ([email protected])