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")# pip install -U transformers accelerate # 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 flax_model.msgpack 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/flax_model.msgpack
- Command line
-
hf download hf://Contrastive-Tension/BERT-Base-Swe-CT-STSb/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Contrastive-Tension/BERT-Base-Swe-CT-STSb/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 871074650405b2883563723f1c8df33270908d45669264ad9ed1224fb802466d
- Size of remote file:
- 499 MB
- SHA256:
- 71d8cdb74a241d80b6bf82fb7eddc763f4f8cf28d9a98eaf20ccc980b8ac15af
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