Instructions to use McGill-NLP/dpr-conversation_encoder-basic_info with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/dpr-conversation_encoder-basic_info with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="McGill-NLP/dpr-conversation_encoder-basic_info")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("McGill-NLP/dpr-conversation_encoder-basic_info") model = AutoModel.from_pretrained("McGill-NLP/dpr-conversation_encoder-basic_info", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from McGill-NLP/dpr-conversation_encoder-basic_info: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/McGill-NLP/dpr-conversation_encoder-basic_info/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://McGill-NLP/dpr-conversation_encoder-basic_info/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/McGill-NLP/dpr-conversation_encoder-basic_info/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- f930c9fc286f98bbcb3f814e01a6bdfa5383b26548f1aa537d4d588e34df5e5d
- Size of remote file:
- 438 MB
- SHA256:
- 51747edefe1559a805a0281abe2d664f21b2eddbe7e48c13574e8a51bfb71c70
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.