Instructions to use vbius01/est-roberta-ud-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vbius01/est-roberta-ud-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vbius01/est-roberta-ud-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vbius01/est-roberta-ud-ner") model = AutoModelForTokenClassification.from_pretrained("vbius01/est-roberta-ud-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from vbius01/est-roberta-ud-ner: direct link, hf CLI and curl.
- Browser
- Download file 930 MB
-
https://huggingface.co/vbius01/est-roberta-ud-ner/resolve/main/optimizer.pt
- Command line
-
hf download hf://vbius01/est-roberta-ud-ner/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/vbius01/est-roberta-ud-ner/resolve/main/optimizer.pt
930 MB
- Xet hash:
- 1467f3596eed963d39ea9a895d5dfa6a16e6877f2cbbfc8e246c2ae1f312f15e
- Size of remote file:
- 930 MB
- SHA256:
- e86bc28e16647ae0f011194122976d10ad414abc558a77ad8fb5c52758d6ed38
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