Instructions to use BadreddineHug/LayoutLM_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLM_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLM_5")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLM_5") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLM_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7eb4dc210763d2ffff7dbf78cd837c9adc49057f9cfff3f8999026f3a44fc8e5
- Size of remote file:
- 504 MB
- SHA256:
- e97d97421458daa43c3e28d532511dcd4dc648f248a89d0d362cb940b9037d94
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