Instructions to use KN123/PromptClassifier-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KN123/PromptClassifier-v1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KN123/PromptClassifier-v1.0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KN123/PromptClassifier-v1.0") model = AutoModelForSequenceClassification.from_pretrained("KN123/PromptClassifier-v1.0") - Notebooks
- Google Colab
- Kaggle
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labels = df_combined['label_text'].unique().tolist()
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labels = [s.strip() for s in labels ]
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- Find the notebook here: https://colab.research.google.com/drive/1pyQB5Olz5E24-wcfPvYDcOUmV3iUcYBW#scrollTo=6hvjXFJtahN8
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```
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labels = df_combined['label_text'].unique().tolist()
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labels = [s.strip() for s in labels ]
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