Instructions to use privacy-tech-lab/LatModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/LatModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/LatModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/LatModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/LatModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a73b708d5bc83f07618368504e6d7df1dd23681f73179802b9f431acdcb71ca
- Size of remote file:
- 57.4 MB
- SHA256:
- d371685ba0434e9c591a62bec4025e631f71f3e4793dc0f7f4e945cb094222c4
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