Instructions to use icpro/trained-model-classification-evaluation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use icpro/trained-model-classification-evaluation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="icpro/trained-model-classification-evaluation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("icpro/trained-model-classification-evaluation") model = AutoModelForSequenceClassification.from_pretrained("icpro/trained-model-classification-evaluation") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dbfd6fdc654b0afa117ee342d4289ba2bce51229eb2911b922fd1e19250fca2b
- Size of remote file:
- 1.35 GB
- SHA256:
- 5b1a12a721001bd50eb6c536b85a2d30a6d81f0e4340cbcca70d56740cd38cee
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