Instructions to use vblagoje/dpr-question_encoder-single-lfqa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vblagoje/dpr-question_encoder-single-lfqa-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="vblagoje/dpr-question_encoder-single-lfqa-base", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vblagoje/dpr-question_encoder-single-lfqa-base") model = AutoModel.from_pretrained("vblagoje/dpr-question_encoder-single-lfqa-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2881c577f9f730fee015aec9a33a46c79bf0fa6e5b7da73c8de14459dec27160
- Size of remote file:
- 438 MB
- SHA256:
- e07916c7ea0e4e50e83798801bfbe3954ef2ec0eec5094ae09f7eafce340b3be
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