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:
- 89c2195e9c4e2726cb8d5155d49e07cc0f482ea65f7b58aaba7e1f2c86ad42c0
- Size of remote file:
- 438 MB
- SHA256:
- adeafeb15c0b9093a53bc4d9926796cc41d5a59a67c41225d07b5cab8506cd46
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