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 tokenizer.json from michaelfeil/ct2fast-LaBSE: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/michaelfeil/ct2fast-LaBSE/resolve/main/tokenizer.json
- Command line
-
hf download hf://michaelfeil/ct2fast-LaBSE/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/michaelfeil/ct2fast-LaBSE/resolve/main/tokenizer.json
13.6 MB
- Xet hash:
- db805ff5bedae176fd48417e425a5c12c608782e99505dd5b75989913ae47f41
- Size of remote file:
- 13.6 MB
- SHA256:
- 5aab105881afc3a73d5c8445cdc5c0302b1c3efdecd71a1a34fa0cf4e5b7bf43
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