Instructions to use bstds/id-roberta-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bstds/id-roberta-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="bstds/id-roberta-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("bstds/id-roberta-ner") model = AutoModelForTokenClassification.from_pretrained("bstds/id-roberta-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from bstds/id-roberta-ner: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/bstds/id-roberta-ner/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://bstds/id-roberta-ner@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/bstds/id-roberta-ner/resolve/refs%2Fpr%2F1/tokenizer.json
17.1 MB
- Xet hash:
- 294ab8134d63966be1d12dd38de5899d77da4db4ae9fecfd1f2ed1511a7072ab
- Size of remote file:
- 17.1 MB
- SHA256:
- 2bae0289b9bb66bfbf85d834f6313ebd8e40f484df2bd0351539e74a0e4ddeb4
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