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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6304de2e46937077a007f7179ac19957e5fb43cf0a6ba351fda779ff03988d32
- Size of remote file:
- 623 Bytes
- SHA256:
- e1943902b01c5006061d1b3c3377e5df8ad6016474efcca36ec96358c450f91a
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