Instructions to use shimogerald/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shimogerald/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="shimogerald/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("shimogerald/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("shimogerald/bert-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4304401dc8de63f6cb722272b0f95c00a77a359f433d8a74dcb2be05a54cf650
- Size of remote file:
- 5.2 kB
- SHA256:
- f736274a535c92fe8458e4f497f24662bbbb32440e05fef789d312a90edab9fe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.