Instructions to use jtlicardo/bert-finetuned-bpmn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jtlicardo/bert-finetuned-bpmn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jtlicardo/bert-finetuned-bpmn")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jtlicardo/bert-finetuned-bpmn") model = AutoModelForTokenClassification.from_pretrained("jtlicardo/bert-finetuned-bpmn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 70e7978574506d39c0e55bb01ece3a98af664c4fda86da985f4b322aca4de21b
- Size of remote file:
- 431 MB
- SHA256:
- 8c675b88deda1370782e8cfbeac2f6362319f2f6e5fe313e8585eb5dd8edfd2c
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