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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d10209e57e944719093dea4535f52d0aef4278b43f73b92e634ca800fcba0d01
- Size of remote file:
- 438 MB
- SHA256:
- 553fce1808af76140ae751f11c030e452c67d68a4def3c920e85809b53245f6c
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