Instructions to use terzimert/bert-finetuned-ner-balancedData_v3.01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use terzimert/bert-finetuned-ner-balancedData_v3.01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="terzimert/bert-finetuned-ner-balancedData_v3.01")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("terzimert/bert-finetuned-ner-balancedData_v3.01") model = AutoModelForTokenClassification.from_pretrained("terzimert/bert-finetuned-ner-balancedData_v3.01", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a8ac46e573798d7887ac17afdaa950281172cfc89e33abd2657f1ee80c49b67
- Size of remote file:
- 709 MB
- SHA256:
- 6180fb18ca08b2a8c99777c03fc0042991eb354781632990c49f9e62fa96b2d5
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