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:
- 97c1087a605b682341aec5a79766425500aeb700d6cdc0c3d79560d625fddaad
- Size of remote file:
- 1.25 kB
- SHA256:
- 91f423adc1e15ce060ad0958db21a713ef41d0b1f4a74736d7a30e5355652f90
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.