Text Classification
Transformers
Safetensors
English
bert
multi-label
theme_detection
mentorship
entrepreneurship
startup success
json automation
text-embeddings-inference
Instructions to use 4nkh/theme_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 4nkh/theme_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="4nkh/theme_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("4nkh/theme_model") model = AutoModelForSequenceClassification.from_pretrained("4nkh/theme_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Verified signature# Theme Model (Multi-Label Classifier) This model performs **multi-label theme detection** for community knowledge submissions. It classifies short narratives or summaries into one or more of the following themes: - **Mentorship** - **Entrepreneurship** - **Startup Success** ## 🧩 Base Model Fine-tuned from [`distilbert-base-uncased`](https://huggingface.co/distilbert-base-uncased). ## 📚 Training Data Trained on a synthetic dataset (`theme_response.json`) consisting of community stories with labeled themes such as: - “Mentorship strengthens community-based skill development.” - “Entrepreneurship drives local economic growth and sustainability.” - “Support programs help boost startup success rates.” ## ⚙️ Usage ```python from transformers import pipeline classifier = pipeline("text-classification", model="4nkh/theme_model", tokenizer="4nkh/theme_model", top_k=None) text = "Our incubator helped women founders scale their startups." preds = classifier(text) print(preds)d9d84f8 -
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