Instructions to use jamesliounis/DataBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jamesliounis/DataBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jamesliounis/DataBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jamesliounis/DataBERT") model = AutoModelForSequenceClassification.from_pretrained("jamesliounis/DataBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f31b9f5034b0825ddb126d0c54a61724896a715345fc2368c6739e28ffa818d
- Size of remote file:
- 438 MB
- SHA256:
- 99700d1bab1a1d7f09fd28d8935343065d7911c497856244d40a3621992c1bc1
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