Instructions to use BadreddineHug/LayoutLM_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BadreddineHug/LayoutLM_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BadreddineHug/LayoutLM_4")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("BadreddineHug/LayoutLM_4") model = AutoModelForTokenClassification.from_pretrained("BadreddineHug/LayoutLM_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c399a39a89af6781f31735ae978a1df60289c5178b7e26827533b308543e88c
- Size of remote file:
- 3.96 kB
- SHA256:
- ffd94855000652cbf08d58f5f51d174b841952dfc82aae80d4dd468f2da398be
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