Instructions to use castorini/wiki-text-8-4-multi-dpr2-query-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/wiki-text-8-4-multi-dpr2-query-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="castorini/wiki-text-8-4-multi-dpr2-query-encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("castorini/wiki-text-8-4-multi-dpr2-query-encoder") model = AutoModel.from_pretrained("castorini/wiki-text-8-4-multi-dpr2-query-encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f583ea427d55ce3f850ab8f40ebeaa38d16f954f5cd3e75dc8494cbc836a4598
- Size of remote file:
- 438 MB
- SHA256:
- b83f7cb287430c19bf7bfcb055ed248c396e44e22c0576978800b28713e5d9ad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.