Instructions to use voidful/dpr-question_encoder-bert-base-multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/dpr-question_encoder-bert-base-multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="voidful/dpr-question_encoder-bert-base-multilingual")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("voidful/dpr-question_encoder-bert-base-multilingual") model = AutoModel.from_pretrained("voidful/dpr-question_encoder-bert-base-multilingual") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd6304896c86332b3b443664c5a72bbe938d24b5e566c26780c21ce583485e24
- Size of remote file:
- 711 MB
- SHA256:
- 3b62e46af74e13e10de1868a19417ef42126da755a63837f32b2bda9d511a404
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