PubTables-1M: Towards comprehensive table extraction from unstructured documents
Paper • 2110.00061 • Published • 3
# Load model directly
from transformers import AutoImageProcessor, AutoModelForObjectDetection
processor = AutoImageProcessor.from_pretrained("aParadigmP/table-transformer-detection-custom-ale")
model = AutoModelForObjectDetection.from_pretrained("aParadigmP/table-transformer-detection-custom-ale")Quick Links
Table Transformer (fine-tuned for Table Detection)
Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents by Smock et al. and first released in this repository.
Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
Model description
The Table Transformer is equivalent to DETR, a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention.
Usage
You can use the raw model for detecting tables in documents. See the documentation for more info.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="aParadigmP/table-transformer-detection-custom-ale")