Token Classification
Transformers
PyTorch
TensorBoard
layoutlmv2
object-detection
vision
Generated from Trainer
DocLayNet
COCO
PDF
IBM
Financial-Reports
Finance
Manuals
Scientific-Articles
Science
Laws
Law
Regulations
Patents
Government-Tenders
image-segmentation
Eval Results (legacy)
Instructions to use pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384") model = AutoModelForTokenClassification.from_pretrained("pierreguillou/layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384") - Notebooks
- Google Colab
- Kaggle
layout-xlm-base-finetuned-with-DocLayNet-base-at-linelevel-ml384 / runs /Mar02_19-25-20_fe43f16895b4 /1677786852.6119568
5.89 kB
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