Instructions to use jtlicardo/distilbert-bpmn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jtlicardo/distilbert-bpmn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jtlicardo/distilbert-bpmn")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jtlicardo/distilbert-bpmn") model = AutoModelForTokenClassification.from_pretrained("jtlicardo/distilbert-bpmn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8e905d9789b26c627e23182587f6b31e089cf69c2715710d783532ab8167771
- Size of remote file:
- 261 MB
- SHA256:
- 0bfff2064d18c0f644629167c501da2caa85750f7cef4df9f9f88a400aeb91f8
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