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 pytorch_model.bin from dslim/bert-base-NER-uncased: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/dslim/bert-base-NER-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dslim/bert-base-NER-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dslim/bert-base-NER-uncased/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- ebc8fe49192eb923fe86a7a501e8a032ca28799fd09d0b7973d6bb1f2a03fff6
- Size of remote file:
- 438 MB
- SHA256:
- a553622c3b8cdd413cf4872ace1880ec6bbab3308a1847dd26cee7692a757dd6
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