Feature Extraction
Transformers
bert
ctranslate2
int8
float16 - bert - sentence_embedding - multilingual - google - sentence-similarity
text-embeddings-inference
Instructions to use michaelfeil/ct2fast-LaBSE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaelfeil/ct2fast-LaBSE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="michaelfeil/ct2fast-LaBSE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("michaelfeil/ct2fast-LaBSE") model = AutoModel.from_pretrained("michaelfeil/ct2fast-LaBSE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.bin from michaelfeil/ct2fast-LaBSE: direct link, hf CLI and curl.
- Browser
- Download file 942 MB
-
https://huggingface.co/michaelfeil/ct2fast-LaBSE/resolve/main/model.bin
- Command line
-
hf download hf://michaelfeil/ct2fast-LaBSE/model.bin
-
curl -L -o model.bin https://huggingface.co/michaelfeil/ct2fast-LaBSE/resolve/main/model.bin
942 MB
- Xet hash:
- 6e42409787e5975c06941f88a6fbd6212f3410873bfb97ebcc662b7db9bbc9f8
- Size of remote file:
- 942 MB
- SHA256:
- 6552294a5f206d78186219707f4fa44c0f90c4c213c2d64f622ea173f5f37fee
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