Instructions to use relevanthint/bert-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use relevanthint/bert-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="relevanthint/bert-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("relevanthint/bert-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("relevanthint/bert-finetuned-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8a59c25fd37ceda233839e64e5e7e7041452d808bc5fc7caf3d79f2dfdcb986
- Size of remote file:
- 431 MB
- SHA256:
- 073263197c70cb7ec731d664554e04958325560b4ae66b4c828bb24ed3f53a3b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.