Instructions to use faisalraza/layoutlm-invoices with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faisalraza/layoutlm-invoices with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="faisalraza/layoutlm-invoices")# Load model directly from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("faisalraza/layoutlm-invoices") model = AutoModelForDocumentQuestionAnswering.from_pretrained("faisalraza/layoutlm-invoices") - Notebooks
- Google Colab
- Kaggle
LayoutLM for Invoices
This is a fine-tuned version of the multi-modal LayoutLM model for the task of question answering on invoices and other documents. It has been fine-tuned on a proprietary dataset of invoices as well as both SQuAD2.0 and DocVQA for general comprehension.
Non-consecutive tokens
Unlike other QA models, which can only extract consecutive tokens (because they predict the start and end of a sequence), this model can predict longer-range, non-consecutive sequences with an additional classifier head. For example, QA models often encounter this failure mode:
Before
After
However this model is able to predict non-consecutive tokens and therefore the address correctly:
Getting started with the model
The best way to use this model is via DocQuery.
About us
This model was created by the team at Impira.
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