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:
- f8a7f9344a48c063618e64fbd8ab1c2cbc1ed5c1b145714dafe3a25bc8c5dd18
- Size of remote file:
- 57.4 MB
- SHA256:
- a1c504cab5a3fe5eb0d8294ccaefafe310b4ffe068cf53a8f591d4272a04828b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.