Instructions to use classla/bcms-bertic-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use classla/bcms-bertic-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="classla/bcms-bertic-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("classla/bcms-bertic-ner") model = AutoModelForTokenClassification.from_pretrained("classla/bcms-bertic-ner") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee858384b53b4cfd4c11f099bc007f859f6bc8b7c41c5c315cf2e2b4c3f14e90
- Size of remote file:
- 440 MB
- SHA256:
- 4edf6824e572a9f6d81a771d8c9e817d2c38225ea965013b02f78fd299dfe20b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.