Instructions to use Contrastive-Tension/BERT-Base-Swe-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-Swe-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-Swe-CT-STSb")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Contrastive-Tension/BERT-Base-Swe-CT-STSb") model = AutoModel.from_pretrained("Contrastive-Tension/BERT-Base-Swe-CT-STSb", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Contrastive-Tension/BERT-Base-Swe-CT-STSb: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Contrastive-Tension/BERT-Base-Swe-CT-STSb/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Contrastive-Tension/BERT-Base-Swe-CT-STSb/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Contrastive-Tension/BERT-Base-Swe-CT-STSb/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- a61b56c4af4642028c99119850f97918f1d6d504c6738ecba3bfd0dd5641dad5
- Size of remote file:
- 499 MB
- SHA256:
- cb0cbeb90fa4624d5187db71c6b5626b6632aa8b52fa85b1e9b1a142bd0cf96d
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