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 (danielamahtoday@gmail.com)