Instructions to use tlemberger/sd-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tlemberger/sd-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tlemberger/sd-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tlemberger/sd-ner") model = AutoModelForTokenClassification.from_pretrained("tlemberger/sd-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3436a7f4eb4dddf6fbc0f3bb2b2e0ac27c59d58aed84f7d7f70f8dc298904c9
- Size of remote file:
- 1.97 kB
- SHA256:
- 38c6773e884e6cd952ff5b4f06298fa5915b3177bcbd6851fcd270e8fbbe3941
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