Instructions to use pritmanvar/ner_bert_uncased_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pritmanvar/ner_bert_uncased_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pritmanvar/ner_bert_uncased_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("pritmanvar/ner_bert_uncased_model") model = AutoModelForTokenClassification.from_pretrained("pritmanvar/ner_bert_uncased_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1e4e730acbe15a9442ae8e7b1dbf36f42152a79beccd12677b51bd540f0c0cb
- Size of remote file:
- 4.92 kB
- SHA256:
- 6121202a4e9b8777eb80f932a91adc66b21743a267c58536775c79c26ca6178c
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