| | --- |
| | library_name: transformers |
| | tags: |
| | - topic |
| | - multi-sentiment |
| | license: mit |
| | datasets: |
| | - valurank/Topic_Classification |
| | language: |
| | - en |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | base_model: |
| | - distilbert/distilbert-base-uncased |
| | --- |
| | |
| | # 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](https://huggingface.co/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 |
| |
|
| | ```python |
| | 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](https://mlco2.github.io/impact#compute)) |
| |
|
| | ## Citation |
| |
|
| | ```bibtex |
| | @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) |
