Instructions to use Shitao/msmarco_query_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shitao/msmarco_query_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Shitao/msmarco_query_encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Shitao/msmarco_query_encoder") model = AutoModel.from_pretrained("Shitao/msmarco_query_encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.189f5b1
-
Upload vocab.txt
0179561 -
Upload tokenizer_config.json
1e3af30 -
Upload tokenizer.json
4333efe -
Upload config.json
061a6de -
Upload pytorch_model.bin with git-lfs
c9dcd90 -
initial commit
d581773