Feature Extraction
Transformers
PyTorch
TensorFlow
Safetensors
Russian
English
bert
pretraining
embeddings
sentence-similarity
Instructions to use cointegrated/LaBSE-en-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/LaBSE-en-ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cointegrated/LaBSE-en-ru")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("cointegrated/LaBSE-en-ru") model = AutoModelForPreTraining.from_pretrained("cointegrated/LaBSE-en-ru", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cointegrated/LaBSE-en-ru: direct link, hf CLI and curl.
- Browser
- Download file 516 MB
-
https://huggingface.co/cointegrated/LaBSE-en-ru/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cointegrated/LaBSE-en-ru/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cointegrated/LaBSE-en-ru/resolve/main/pytorch_model.bin
516 MB
- Xet hash:
- 2528e57849de83642e0fa7f8c762d15fa9b24ea970ce02b8ca216da18e951352
- Size of remote file:
- 516 MB
- SHA256:
- d929e16c4cc9b40cdd96219e8ce3c1084129798435b3c67212efd68fa018673b
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