Instructions to use PolarNight/deberta-base-ner-hw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PolarNight/deberta-base-ner-hw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="PolarNight/deberta-base-ner-hw")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("PolarNight/deberta-base-ner-hw") model = AutoModelForTokenClassification.from_pretrained("PolarNight/deberta-base-ner-hw", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from PolarNight/deberta-base-ner-hw: direct link, hf CLI and curl.
- Browser
- Download file 555 MB
-
https://huggingface.co/PolarNight/deberta-base-ner-hw/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://PolarNight/deberta-base-ner-hw/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/PolarNight/deberta-base-ner-hw/resolve/main/pytorch_model.bin
555 MB
- Xet hash:
- d3f8ba51e2eefcc7ebb00ba57d7897b0c3d2f3865d8bb120e078f69e55babaa6
- Size of remote file:
- 555 MB
- SHA256:
- d7ec0bdd6563da2950d255cecde10e42f25ab7317d40304fe537587489d85c3a
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