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_12-49-31_6f61ebd14765 /events.out.tfevents.1677761390.6f61ebd14765.170.0
- Xet hash:
- 853de99c6c849c05e4be4ddc6a3f3de20a86e2fc3810069087bd8319cad6974d
- Size of remote file:
- 8.18 kB
- SHA256:
- ece513cc3bfe0c8d07c895057bb433eb6f5f9efea52f0fa549a7b858d287715e
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