Instructions to use universalner/uner_dan_ddt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalner/uner_dan_ddt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="universalner/uner_dan_ddt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("universalner/uner_dan_ddt") model = AutoModelForTokenClassification.from_pretrained("universalner/uner_dan_ddt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from universalner/uner_dan_ddt: direct link, hf CLI and curl.
- Browser
- Download file 2.24 GB
-
https://huggingface.co/universalner/uner_dan_ddt/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://universalner/uner_dan_ddt/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/universalner/uner_dan_ddt/resolve/main/pytorch_model.bin
2.24 GB
- Xet hash:
- bde68c81aa70db241df6305a7e0d152a58e78a37a033312e61635e70cb3f0a4a
- Size of remote file:
- 2.24 GB
- SHA256:
- 0c58ceaaac19789e4b343067a0e34f087b2fd25603de3867ffc2b629b12b4af3
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