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:
- 29ea00ba91a1f9f858ddea6153e899f73ea814a50c39578505f6a84e238b052b
- Size of remote file:
- 4.34 kB
- SHA256:
- 66fa861d99be4fd0989269d339c719adbc03c1da9a1e621f04beff1098bc9b50
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