Instructions to use devanshrj/roberta-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devanshrj/roberta-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="devanshrj/roberta-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("devanshrj/roberta-ner") model = AutoModelForTokenClassification.from_pretrained("devanshrj/roberta-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56f154ee431a3f67e7ce4ba6beff7b0f3023e834e406ce92e3f9896f7bc22bc2
- Size of remote file:
- 496 MB
- SHA256:
- 2b1246e99563578017104c394c8f5a47d7be3ebc1178e5fa8dc3804a09f252b4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.