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 pytorch_model.bin from baikalai/dbert-ner: direct link, hf CLI and curl.
- Browser
- Download file 449 MB
-
https://huggingface.co/baikalai/dbert-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://baikalai/dbert-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/baikalai/dbert-ner/resolve/main/pytorch_model.bin
449 MB
- Xet hash:
- 6b7705a1eaac8594c334963e8e1bc8497d3789a9faddfa12459a52d508b6d118
- Size of remote file:
- 449 MB
- SHA256:
- 8c12bcc8f2f2dc515f2f7e47f43e80fbd5d5954fbd897ed458dfdfb8f45540b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.