Instructions to use viswavi/datafinder-huggingface-prompt-queries with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use viswavi/datafinder-huggingface-prompt-queries with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="viswavi/datafinder-huggingface-prompt-queries")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("viswavi/datafinder-huggingface-prompt-queries") model = AutoModel.from_pretrained("viswavi/datafinder-huggingface-prompt-queries") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ec2c56faff1d0ae08fbe450f7330d011f896676e43c95079b709c2b0d00b3b0
- Size of remote file:
- 440 MB
- SHA256:
- b9a54d64632e0c893403a583b87dd9a5c1b31ef5429207c324303f1d79ecea4f
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