Instructions to use privacy-tech-lab/LatBaseModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use privacy-tech-lab/LatBaseModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="privacy-tech-lab/LatBaseModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("privacy-tech-lab/LatBaseModel") model = AutoModelForSequenceClassification.from_pretrained("privacy-tech-lab/LatBaseModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f26c4a897d5ecb098216070515fefc1db06aad11f804fa7110875fb21340da1
- Size of remote file:
- 438 MB
- SHA256:
- 8e72f8d8eff8c1f0464a72d9b48c14231e8e7b2dd1d08f07eb5013bd9532936d
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