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 flax_model.msgpack 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/flax_model.msgpack
- Command line
-
hf download hf://DeepPavlov/bert-base-multilingual-cased-sentence/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/DeepPavlov/bert-base-multilingual-cased-sentence/resolve/main/flax_model.msgpack
711 MB
- Xet hash:
- 4ba59bd45f9e587bb929a31ce84c59a2052f7da878fe8198e47e0fedcedaaeb9
- Size of remote file:
- 711 MB
- SHA256:
- 9f32138e62118a998cf650ed04a20a0508b3b92807b03fee231b7e88d9a64d3f
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