Instructions to use mpalaval/bert-ner-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mpalaval/bert-ner-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mpalaval/bert-ner-3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("mpalaval/bert-ner-3") model = AutoModelForTokenClassification.from_pretrained("mpalaval/bert-ner-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7a769b624c56eea9e4fec6413a0a9179d0f747e2fc12bb48fad81ac0278e3e0
- Size of remote file:
- 4.54 kB
- SHA256:
- 7d21b81013a9c3b48f7763d248dcbc517b800c4a4c88d3cde7dc638aad719ceb
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