Instructions to use devrunner09/classification-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devrunner09/classification-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="devrunner09/classification-model")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("devrunner09/classification-model") model = AutoModel.from_pretrained("devrunner09/classification-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ee563c62b5310679ce4af926c203134f4a6fa2a3a948826705fa7e60c2d62b1
- Size of remote file:
- 1.11 GB
- SHA256:
- f5b02dac73d88601cb20ee9abb77a227b65fbfb9c3527a2942abe047ee8d34c0
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