Instructions to use JanSt/albert-base-v2_mbti-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JanSt/albert-base-v2_mbti-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JanSt/albert-base-v2_mbti-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JanSt/albert-base-v2_mbti-classification") model = AutoModelForSequenceClassification.from_pretrained("JanSt/albert-base-v2_mbti-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d0e8465189940bdf6f42c0d88a814219f1b3a329d5c9b0d3e269b4f04944c4f
- Size of remote file:
- 46.8 MB
- SHA256:
- b1e92435cfefc360a88297fd6232aae1014995dbd85fed81e1ac79b69b8ed977
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