Instructions to use DeepPavlov/bert-base-multilingual-cased-sentence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepPavlov/bert-base-multilingual-cased-sentence with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DeepPavlov/bert-base-multilingual-cased-sentence")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DeepPavlov/bert-base-multilingual-cased-sentence") model = AutoModel.from_pretrained("DeepPavlov/bert-base-multilingual-cased-sentence", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from DeepPavlov/bert-base-multilingual-cased-sentence: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/DeepPavlov/bert-base-multilingual-cased-sentence/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://DeepPavlov/bert-base-multilingual-cased-sentence/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/DeepPavlov/bert-base-multilingual-cased-sentence/resolve/main/pytorch_model.bin
711 MB
- Xet hash:
- 6dc0e850a388d46fa9244daf659fe630ddd91e379f647b6e113c4fe64646eb31
- Size of remote file:
- 711 MB
- SHA256:
- 860df314bc1ec4ababaed0d7d1fd1ff35e5d5eb0fafd6d14428df4e77b3e7539
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