Instructions to use izaitova/herbert-large-cased-upos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use izaitova/herbert-large-cased-upos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="izaitova/herbert-large-cased-upos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("izaitova/herbert-large-cased-upos") model = AutoModelForTokenClassification.from_pretrained("izaitova/herbert-large-cased-upos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from izaitova/herbert-large-cased-upos: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/izaitova/herbert-large-cased-upos/resolve/main/training_args.bin
- Command line
-
hf download hf://izaitova/herbert-large-cased-upos/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/izaitova/herbert-large-cased-upos/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- c5d9556ba677be531172165e77ae37a0d816b42bb4543c08cc769c764e030a96
- Size of remote file:
- 5.24 kB
- SHA256:
- 14b6ab969fe13507814a64869d936e38c11d50cd24c1d8b1a5833c2b03c82597
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