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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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model-index:
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- name: theme_model
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results: []
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---
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- Transformers 4.57.3
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- Pytorch 2.8.0
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- Datasets 4.4.2
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- Tokenizers 0.22.2
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model-index:
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- name: theme_model
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results: []
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datasets:
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- 4nkh/theme_data
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- Transformers 4.57.3
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- Pytorch 2.8.0
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- Datasets 4.4.2
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- Tokenizers 0.22.2
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