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("grays-ai/table-transformer-structure-recognition")
model = AutoModelForObjectDetection.from_pretrained("grays-ai/table-transformer-structure-recognition")Quick Links
Table Transformer (fine-tuned for Table Structure Recognition)
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 the structure (like rows, columns) in tables. See the documentation for more info.
- Downloads last month
- 9
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="grays-ai/table-transformer-structure-recognition")