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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tlemberger/sd-ner: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/tlemberger/sd-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tlemberger/sd-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tlemberger/sd-ner/resolve/main/pytorch_model.bin
496 MB
- Xet hash:
- 09c7676210872191846dd7b6abf3877560aef6d6be78995ca67f04bc6bca34a6
- Size of remote file:
- 496 MB
- SHA256:
- c25faa39c646e82e3d8b104872f73c12127cea6e938eabfdcddcfaa5b4024b76
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