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:
- 98729348591ecca7aed09780d0a679a869a6f68adeac72fd3ec083aef7f837dc
- Size of remote file:
- 3.96 kB
- SHA256:
- bf06ea5b33b6077b50adfbf16e772b80619dea32059baf97cdb2f4dd0559434d
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