Instructions to use osmanh/bert-base-NER-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osmanh/bert-base-NER-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="osmanh/bert-base-NER-model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("osmanh/bert-base-NER-model") model = AutoModelForTokenClassification.from_pretrained("osmanh/bert-base-NER-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b0409be8a7fcb22ec99e5a3108f76d3b39cd0492c57a0d0946da72917deba4cc
- Size of remote file:
- 5.3 kB
- SHA256:
- 8fcf4dbd6391dafef09a3eb8d2f9743939351d4ce9e647135765ff3ed60b1cf9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.