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:
- dc9afb050ee43e30b784c1ce9307474fea918703bdb5ec6ef0f800c3738ad0df
- Size of remote file:
- 436 MB
- SHA256:
- d385d4e8a6fd0c04dff5ab0ef9b091d66ad239a1d17ef4c6fee85d860bb6efee
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