Instructions to use JulienRPA/BERT_SPARQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JulienRPA/BERT_SPARQL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="JulienRPA/BERT_SPARQL")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("JulienRPA/BERT_SPARQL") model = AutoModelForMaskedLM.from_pretrained("JulienRPA/BERT_SPARQL") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1806156dbeaea80b8fc0cbdfd2bcd5e48bb5acc87466ceecb0541caa58156cac
- Size of remote file:
- 444 MB
- SHA256:
- 9dce214e67a2dff614d0b504ff7783f95aae900ca457cf848595e363f64371d9
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