Instructions to use Yanhao/simcse-bert-large-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yanhao/simcse-bert-large-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Yanhao/simcse-bert-large-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Yanhao/simcse-bert-large-uncased") model = AutoModel.from_pretrained("Yanhao/simcse-bert-large-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Yanhao/simcse-bert-large-uncased: direct link, hf CLI and curl.
- Browser
- Download file 2.1 kB
-
https://huggingface.co/Yanhao/simcse-bert-large-uncased/resolve/main/training_args.bin
- Command line
-
hf download hf://Yanhao/simcse-bert-large-uncased/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Yanhao/simcse-bert-large-uncased/resolve/main/training_args.bin
2.1 kB
- Xet hash:
- 760767eb48ab57f2acdd6857db15b1ec6bc959ab814e01fafaa89f4704e595fd
- Size of remote file:
- 2.1 kB
- SHA256:
- dd714dd2a570835564dd56f92dea60cb052896bc4400116ea89eec00e78cc8dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.