Instructions to use privacy-tech-lab/LatBaseModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/LatBaseModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/LatBaseModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/LatBaseModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/LatBaseModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ccba1c9b413a52d03ddbad73b8b6f3ef42b7709ffe00972675b1f1649f573ab
- Size of remote file:
- 3.06 kB
- SHA256:
- abe3a2507054cf9acd39b77dae1b93b66a0560e8f5cc37834f6d329a398634d5
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