Instructions to use princeton-nlp/unsup-simcse-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use princeton-nlp/unsup-simcse-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="princeton-nlp/unsup-simcse-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/unsup-simcse-roberta-base") model = AutoModel.from_pretrained("princeton-nlp/unsup-simcse-roberta-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from princeton-nlp/unsup-simcse-roberta-base: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/princeton-nlp/unsup-simcse-roberta-base/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://princeton-nlp/unsup-simcse-roberta-base/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/princeton-nlp/unsup-simcse-roberta-base/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 32c754e9e74622e61cad1929a2013ab9ba472d7d24cfdbd805f8e1659c9e3887
- Size of remote file:
- 499 MB
- SHA256:
- 3547b8fa977dace87c402eb2bf65dda30810a8e436e24f34a1771ebc6391868e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.