Instructions to use BadreddineHug/LayoutLM_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLM_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLM_1")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLM_1") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLM_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4158eda4d419bc311f7e6559f5581c0d8a5a502eeadb78cff086d2e5d6baf3b5
- Size of remote file:
- 3.96 kB
- SHA256:
- 470c0cea7f228a1724461746da2d2da017b0cd7ccb8cf1104a95eee897e11098
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