Instructions to use shellypeng/distillbert-base-cased-finetuned-ner4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shellypeng/distillbert-base-cased-finetuned-ner4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="shellypeng/distillbert-base-cased-finetuned-ner4")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("shellypeng/distillbert-base-cased-finetuned-ner4") model = AutoModelForTokenClassification.from_pretrained("shellypeng/distillbert-base-cased-finetuned-ner4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ec88f7ff857419a68c0cca7f443620e097591c7a40ca1b8bc981806e8b5e5bf
- Size of remote file:
- 5.43 kB
- SHA256:
- 7e5d270c1d7dd1a78899a22f174d54bb74bdfcf4b5f3015ff06bbb11068c718b
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