Instructions to use bassie96code/RobBERT-LLM-wettekst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bassie96code/RobBERT-LLM-wettekst with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bassie96code/RobBERT-LLM-wettekst")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bassie96code/RobBERT-LLM-wettekst") model = AutoModelForTokenClassification.from_pretrained("bassie96code/RobBERT-LLM-wettekst", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bassie96code/RobBERT-LLM-wettekst: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/bassie96code/RobBERT-LLM-wettekst/resolve/main/training_args.bin
- Command line
-
hf download hf://bassie96code/RobBERT-LLM-wettekst/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bassie96code/RobBERT-LLM-wettekst/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- 7134489b25cfc3bb7c32e5c3cdec79ab3e75b75eeaa3ee0950aa0f7f51087e03
- Size of remote file:
- 4.6 kB
- SHA256:
- 0e9c8b5599bf9235dbb480a9ddf6d5d37683ecf2e308e9eca886f34d517cf90e
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