Instructions to use dtorber/bert-base-multilingual-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dtorber/bert-base-multilingual-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dtorber/bert-base-multilingual-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dtorber/bert-base-multilingual-cased") model = AutoModelForSequenceClassification.from_pretrained("dtorber/bert-base-multilingual-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dtorber/bert-base-multilingual-cased: direct link, hf CLI and curl.
- Browser
- Download file 5.05 kB
-
https://huggingface.co/dtorber/bert-base-multilingual-cased/resolve/main/training_args.bin
- Command line
-
hf download hf://dtorber/bert-base-multilingual-cased/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dtorber/bert-base-multilingual-cased/resolve/main/training_args.bin
5.05 kB
- Xet hash:
- 13494bf0ddd606997bb00bfb46fa6e05b899b34e3cc3229126f9b884852f962e
- Size of remote file:
- 5.05 kB
- SHA256:
- cb12985651eff4bf4a2270940b1aea2eda5b8c37671113105e9c4ac4abc7ffa8
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