Instructions to use jcbao77/trained_bert_on_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jcbao77/trained_bert_on_ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jcbao77/trained_bert_on_ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jcbao77/trained_bert_on_ner") model = AutoModelForTokenClassification.from_pretrained("jcbao77/trained_bert_on_ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jcbao77/trained_bert_on_ner: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/jcbao77/trained_bert_on_ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jcbao77/trained_bert_on_ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jcbao77/trained_bert_on_ner/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 853c4dbdeabd66aed30256bea432945c55f5565828e2ee93f514a1665c37e0e7
- Size of remote file:
- 431 MB
- SHA256:
- 6adc6a68ce1f3f945793960b32ef2ba8920f36285495fee8d823071deacb674e
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