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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- adcf1f61030c94c810625a3a076021806b5cd085739bfa8a25c29ba79623a685
- Size of remote file:
- 712 MB
- SHA256:
- a6d93fb91b152f2d98bc31f41d29e9c6373130a38bfc8fec5c03036f251e90e6
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