Instructions to use privacy-tech-lab/LngBaseModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/LngBaseModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/LngBaseModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/LngBaseModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/LngBaseModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93910bf1db88c4973190141aac117a77dbd642451ee01ceed9950fea12568642
- Size of remote file:
- 438 MB
- SHA256:
- 7f84e0041f91c9f8599075bf1d76af54bcee3a5463b4ef863f5fb1c07dfc7d58
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