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:
- e6b91a7da6adf8853712d490a4c2bd2df94f784fe5cdec0ba604e89700f9558b
- Size of remote file:
- 3.52 kB
- SHA256:
- c28ca9e6494a6844b7509cc2ab20f1303e6acc2a21f1aad34c5ac557af68fef9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.