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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("4nkh/theme_model") model = AutoModelForSequenceClassification.from_pretrained("4nkh/theme_model") - Notebooks
- Google Colab
- Kaggle
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### Dataset Description
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<!-- Provide a longer summary of what this dataset is. -->
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This dataset contains short narrative passages (original_text) with associated metadata and labels.
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### Direct Use
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### Dataset Description
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<!-- Provide a longer summary of what this dataset is. -->
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This dataset contains short narrative passages (original_text) with associated metadata and labels.
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The primary target is themes,
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a multi-label list of theme tags used to train a theme classification model.
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1. Startup Success
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2. Mentorship
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3. Entrepreneurship
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A secondary label tone may be used to train a tone classifier.
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### Direct Use
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