Instructions to use Vishal24/bert-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vishal24/bert-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Vishal24/bert-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Vishal24/bert-ner") model = AutoModelForTokenClassification.from_pretrained("Vishal24/bert-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97fe6b483692c9b364f33129202106bcd22dd58bbdf80484fe870cf737d3facc
- Size of remote file:
- 862 MB
- SHA256:
- d4e0a7bce7d7d89e8783efccc7a1906bf73afdf549c6ef81eb00b9261e058f76
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