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:
- 2d79b9eec8f7ad2b7a91870992354d3acb98b4cc28a1b206a27415d6483bd40e
- Size of remote file:
- 496 MB
- SHA256:
- 23e0f789c4143cda3ab1f17652e4cafe656c876bba6aa041c5501a00b4cd63ed
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