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:
- 502a90751a87df43a69d0336cd52a947cc5a24d33ecf78ac55ade4a00d96b6c6
- Size of remote file:
- 3.06 kB
- SHA256:
- 82467f927c1e0b107d63489af36af6a195a1bfbb26627431b308c33f6f3a9b46
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