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 tokenizer_config.json from baikalai/dbert-ner: direct link, hf CLI and curl.
- Browser
- Download file 56 Bytes
-
https://huggingface.co/baikalai/dbert-ner/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://baikalai/dbert-ner/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/baikalai/dbert-ner/resolve/main/tokenizer_config.json
56 Bytes
| { | |
| "do_lower_case": false, | |
| "model_max_length": 128 | |
| } | |