Instructions to use EMBO/sd-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EMBO/sd-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="EMBO/sd-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("EMBO/sd-ner") model = AutoModelForTokenClassification.from_pretrained("EMBO/sd-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from EMBO/sd-ner: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/EMBO/sd-ner/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://EMBO/sd-ner/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/EMBO/sd-ner/resolve/main/flax_model.msgpack
496 MB
- Xet hash:
- 7054902f579ad48a002314e30380caeca698b751cba87582ccd24b0d6c080f44
- Size of remote file:
- 496 MB
- SHA256:
- 8f73900b7994ae25c83a59d9d662d1c404e391ad2ebd236850b5f5b4d4b95da6
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