|
|
--- |
|
|
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) |
