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
Update README.md
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README.md
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pretty_name: Knowledge Theme Training Model
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size_categories:
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- n<1K
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---
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## Dataset Details
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title = {Kuumba Knowledge Theme Training Data},
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year = {2025},
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publisher = {Hugging Face},
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}
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pretty_name: Knowledge Theme Training Model
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size_categories:
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- n<1K
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datasets:
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- 4nkh/theme_data
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metrics:
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- bertscore
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base_model:
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- google-bert/bert-base-uncased
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pipeline_tag: text-classification
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---
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## Dataset Details
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title = {Kuumba Knowledge Theme Training Data},
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year = {2025},
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publisher = {Hugging Face},
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}
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