Instructions to use dsksd/dpr-question_encoder-single-qrecc-model-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dsksd/dpr-question_encoder-single-qrecc-model-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dsksd/dpr-question_encoder-single-qrecc-model-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dsksd/dpr-question_encoder-single-qrecc-model-base") model = AutoModel.from_pretrained("dsksd/dpr-question_encoder-single-qrecc-model-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a7faf450221cfd008f560354a5908be02d645b73c8deaad937bfc45c1890092
- Size of remote file:
- 438 MB
- SHA256:
- d06eb59f95476f4e97730c22a404c2bdab78a0addb2b3a03b9b2c9cc326507e3
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