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:
- 1bae464a4e36227dee664f04033b3357c0ed28d9ca5eaa00da2492eaf6b46842
- Size of remote file:
- 431 MB
- SHA256:
- a0935cda0fdb566ac0777cfdd353f5eb0d08a9161d5611a405ddc490a6c8592e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.