Instructions to use jaggernaut007/bert-base-NER-finetuned-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaggernaut007/bert-base-NER-finetuned-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jaggernaut007/bert-base-NER-finetuned-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jaggernaut007/bert-base-NER-finetuned-ner") model = AutoModelForTokenClassification.from_pretrained("jaggernaut007/bert-base-NER-finetuned-ner") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b126d4dfb306496b8dd3f1069126222a52379549f98f46ee6d63debd5b5d4dfa
- Size of remote file:
- 431 MB
- SHA256:
- 8e104fa5118a92971dacb2dcb400d3f1d8a5cc17afd1518ae21d81fdd9b32a63
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.