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 pytorch_model.bin from Yanhao/simcse-bert-large-uncased: direct link, hf CLI and curl.
- Browser
- Download file 1.34 GB
-
https://huggingface.co/Yanhao/simcse-bert-large-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Yanhao/simcse-bert-large-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Yanhao/simcse-bert-large-uncased/resolve/main/pytorch_model.bin
1.34 GB
- Xet hash:
- 66b6e405edb57d712ecc5929c6b17a8e1f2dcfa9ec3762f78a4416e2aaad19bd
- Size of remote file:
- 1.34 GB
- SHA256:
- 4bc02d6a52d7c39031e5aedb9e62cec405fbd65a195306b77e91c36e436fea2b
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