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
- Xet hash:
- 143dedad0425ad8f6117df2fdf72d136ac84f0eb14cfcbed4feb0b2e62fcf22c
- Size of remote file:
- 438 MB
- SHA256:
- 3cdb2adcb0acaa5d888d682dee5fed8cad6bd7df1d09befd99e1ad85ccb7e053
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