Instructions to use Contrastive-Tension/BERT-Base-CT-STSb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Contrastive-Tension/BERT-Base-CT-STSb with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Contrastive-Tension/BERT-Base-CT-STSb")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Contrastive-Tension/BERT-Base-CT-STSb") model = AutoModel.from_pretrained("Contrastive-Tension/BERT-Base-CT-STSb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Contrastive-Tension/BERT-Base-CT-STSb: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Contrastive-Tension/BERT-Base-CT-STSb/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Contrastive-Tension/BERT-Base-CT-STSb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Contrastive-Tension/BERT-Base-CT-STSb/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 09d38151a673db815bea382d3574f986717f6e390837ca6aace165295915dddf
- Size of remote file:
- 438 MB
- SHA256:
- afdc7b6c11e47656f859a17f8a75d0762112857dfc07663a4ca5c6ed94ad3761
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