Instructions to use baikalai/dbert-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use baikalai/dbert-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="baikalai/dbert-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("baikalai/dbert-ner") model = AutoModelForTokenClassification.from_pretrained("baikalai/dbert-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from baikalai/dbert-ner: direct link, hf CLI and curl.
- Browser
- Download file 1.46 kB
-
https://huggingface.co/baikalai/dbert-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://baikalai/dbert-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/baikalai/dbert-ner/resolve/main/training_args.bin
1.46 kB
- Xet hash:
- e50b950c746a82641e74998a2997b8dcf1739c8a9d733deec20e51f6f331c2ec
- Size of remote file:
- 1.46 kB
- SHA256:
- 24513437c7e13119c66f0846d1218fe61a451f02a1153d92bd785948ba413ae0
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