Instructions to use KevSun/Personality_LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KevSun/Personality_LM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="KevSun/Personality_LM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("KevSun/Personality_LM") model = AutoModelForSequenceClassification.from_pretrained("KevSun/Personality_LM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ebfd595c71294e721579bb53b71a88cab7a4338f16a2ad748774da5ec18ecc1a
- Size of remote file:
- 499 MB
- SHA256:
- 54b8aa4099bbe5768f9d7d549850f2991da75fa889137e5e640fcf9a3d57774d
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