Instructions to use castorini/wiki-text-100w-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-100w-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-100w-multi-dpr2-query-encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("castorini/wiki-text-100w-multi-dpr2-query-encoder") model = AutoModel.from_pretrained("castorini/wiki-text-100w-multi-dpr2-query-encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbb18d767f5812a735c6e402ea371cc77b38e2080d7e7222490ec611aaed1886
- Size of remote file:
- 438 MB
- SHA256:
- 39af63e93acc17c6f35e993b36e799262d6889fde23437451746b9854ef511ab
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.