Instructions to use princeton-nlp/sup-simcse-bert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use princeton-nlp/sup-simcse-bert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="princeton-nlp/sup-simcse-bert-base-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/sup-simcse-bert-base-uncased") model = AutoModel.from_pretrained("princeton-nlp/sup-simcse-bert-base-uncased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from princeton-nlp/sup-simcse-bert-base-uncased: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased/resolve/refs%2Fpr%2F3/pytorch_model.bin
- Command line
-
hf download hf://princeton-nlp/sup-simcse-bert-base-uncased@refs/pr/3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/princeton-nlp/sup-simcse-bert-base-uncased/resolve/refs%2Fpr%2F3/pytorch_model.bin
438 MB
- Xet hash:
- 32dc28cdca4e5c32282d8db9026f8e68064c5415e9ad7598b647a3b811569a61
- Size of remote file:
- 438 MB
- SHA256:
- 2834abc1b9961124fd0b3134c8cf0bc1144b5fc3a4f339d16d6b6c16d25c2004
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