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:
- ff07ab96681c9936dd8ad3435849955a630cabf60ed02ea10881e9aec69aefc2
- Size of remote file:
- 3.52 kB
- SHA256:
- 207754ca33e443a18afc4586e275855acc9d94045acad7ec34deaa2d07b0eec0
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