Instructions to use FrinzTheCoder/bert-base-multilingual-cased-swa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FrinzTheCoder/bert-base-multilingual-cased-swa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FrinzTheCoder/bert-base-multilingual-cased-swa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("FrinzTheCoder/bert-base-multilingual-cased-swa") model = AutoModelForSequenceClassification.from_pretrained("FrinzTheCoder/bert-base-multilingual-cased-swa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from FrinzTheCoder/bert-base-multilingual-cased-swa: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/FrinzTheCoder/bert-base-multilingual-cased-swa/resolve/main/training_args.bin
- Command line
-
hf download hf://FrinzTheCoder/bert-base-multilingual-cased-swa/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/FrinzTheCoder/bert-base-multilingual-cased-swa/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 8885e18209848b80fecb50e3f251e7bc663135c11de1dfd17a85de585ec6fe1d
- Size of remote file:
- 5.37 kB
- SHA256:
- 4ea632b4d836293500ae31a0fa49968533838322772a786409d35791c0e24166
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