Instructions to use jtlicardo/bpmn-information-extraction-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jtlicardo/bpmn-information-extraction-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="jtlicardo/bpmn-information-extraction-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("jtlicardo/bpmn-information-extraction-v2") model = AutoModelForTokenClassification.from_pretrained("jtlicardo/bpmn-information-extraction-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e03747cabccb8fab766262bd788f9fe15534e13d4c7384d0960ae43364586db0
- Size of remote file:
- 431 MB
- SHA256:
- cf1127016f0c966efb6bd819e1e4634bdde3552560fe91e9c681f5a3bbd232c1
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