Instructions to use dslim/bert-base-NER-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dslim/bert-base-NER-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dslim/bert-base-NER-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER-uncased") model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER-uncased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from dslim/bert-base-NER-uncased: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/dslim/bert-base-NER-uncased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://dslim/bert-base-NER-uncased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/dslim/bert-base-NER-uncased/resolve/main/flax_model.msgpack
436 MB
- Xet hash:
- b506d54fc47f6c4b6583087e99c6c37972aa6493e8f46f91bfba3e5870fa2e3d
- Size of remote file:
- 436 MB
- SHA256:
- 16354f635ac87a7523fb1979c8ad2f790a1729b2b96bbb9dde91164b02c2fcd8
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