Instructions to use pragnakalp/bert_based_ner_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pragnakalp/bert_based_ner_models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="pragnakalp/bert_based_ner_models")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("pragnakalp/bert_based_ner_models") model = AutoModelForMaskedLM.from_pretrained("pragnakalp/bert_based_ner_models", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11ffc4e5e1c5cda320f3ca38fb30280ba3b3f1b2b2071f706fc547d83fb90d62
- Size of remote file:
- 433 MB
- SHA256:
- 0dacd0c63a903a2f84b7da6008b2b8cc72f9b4eb16ecf97a3d46dc4ec79e3484
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