Instructions to use jb2k/bert-base-multilingual-cased-language-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jb2k/bert-base-multilingual-cased-language-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jb2k/bert-base-multilingual-cased-language-detection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jb2k/bert-base-multilingual-cased-language-detection") model = AutoModelForSequenceClassification.from_pretrained("jb2k/bert-base-multilingual-cased-language-detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jb2k/bert-base-multilingual-cased-language-detection: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/jb2k/bert-base-multilingual-cased-language-detection/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jb2k/bert-base-multilingual-cased-language-detection/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/jb2k/bert-base-multilingual-cased-language-detection/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- 07767085e609a3e21ccf96accba27d302b167aa9ab9b40b80aea29d32a1c5e7c
- Size of remote file:
- 712 MB
- SHA256:
- 3d37b3d75938317c87f6fdb040fc63e22818d29ffa9c357d6ca5b36c2b6a4733
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