Instructions to use avichr/heBERT_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avichr/heBERT_NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="avichr/heBERT_NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_NER") model = AutoModelForTokenClassification.from_pretrained("avichr/heBERT_NER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 29b14c1122fdc376e0a9047cdbe343e5cfdf8d8014849864873ab34009b21ccb
- Size of remote file:
- 1.78 kB
- SHA256:
- 9d683997fff536a8cb258bffa83dc61ab5f5b2167c2bb0fbd8408703c1e902c4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.