Instructions to use privacy-tech-lab/LngModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/LngModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/LngModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/LngModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/LngModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c6d2253c27072346cf2773fbee16c96a17692123a641e36583f14b58a5f01c3
- Size of remote file:
- 57.4 MB
- SHA256:
- 4d9691ecdd7bc14e44cca57b7e332339454e23471c119fde24d05bf165f60ff2
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