diff --git a/README.md b/README.md index d9aec21..f09c45c 100644 --- a/README.md +++ b/README.md @@ -1,48 +1,57 @@ --- +language: en +thumbnail: https://uploads-ssl.webflow.com/5e3898dff507782a6580d710/614a23fcd8d4f7434c765ab9_logo.png license: mit -tags: -- generated_from_keras_callback -model-index: -- name: layoutlm-document-qa - results: [] --- - +# LayoutLM for Visual Question Answering -# layoutlm-document-qa +This is a fine-tuned version of the multi-modal [LayoutLM](https://aka.ms/layoutlm) model for the task of question answering on documents. It has been fine-tuned on -This model is a fine-tuned version of [impira/layoutlm-document-qa](https://huggingface.co/impira/layoutlm-document-qa) on an unknown dataset. -It achieves the following results on the evaluation set: +## Model details +The LayoutLM model was developed at Microsoft ([paper](https://arxiv.org/abs/1912.13318)) as a general purpose tool for understanding documents. This model is a fine-tuned checkpoint of [LayoutLM-Base-Cased](https://huggingface.co/microsoft/layoutlm-base-uncased), using both the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) and [DocVQA](https://www.docvqa.org/) datasets. -## Model description +## Getting started with the model -More information needed +To run these examples, you must have [PIL](https://pillow.readthedocs.io/en/stable/installation.html), [pytesseract](https://pypi.org/project/pytesseract/), and [PyTorch](https://pytorch.org/get-started/locally/) installed in addition to [transformers](https://huggingface.co/docs/transformers/index). -## Intended uses & limitations +```python +from transformers import AutoTokenizer, pipeline -More information needed +tokenizer = AutoTokenizer.from_pretrained( + "impira/layoutlm-document-qa", + add_prefix_space=True, + trust_remote_code=True, +) -## Training and evaluation data +nlp = pipeline( + model="impira/layoutlm-document-qa", + tokenizer=tokenizer, + trust_remote_code=True, +) -More information needed +nlp( + "https://templates.invoicehome.com/invoice-template-us-neat-750px.png", + "What is the invoice number?" +) +# {'score': 0.9943977, 'answer': 'us-001', 'start': 15, 'end': 15} -## Training procedure +nlp( + "https://miro.medium.com/max/787/1*iECQRIiOGTmEFLdWkVIH2g.jpeg", + "What is the purchase amount?" +) +# {'score': 0.9912159, 'answer': '$1,000,000,000', 'start': 97, 'end': 97} -### Training hyperparameters +nlp( + "https://www.accountingcoach.com/wp-content/uploads/2013/10/income-statement-example@2x.png", + "What are the 2020 net sales?" +) +# {'score': 0.59147286, 'answer': '$ 3,750', 'start': 19, 'end': 20} +``` -The following hyperparameters were used during training: -- optimizer: None -- training_precision: float32 +**NOTE**: This model relies on a [model definition](https://github.com/huggingface/transformers/pull/18407) and [pipeline](https://github.com/huggingface/transformers/pull/18414) that are currently in review to be included in the transformers project. In the meantime, you'll have to use the `trust_remote_code=True` flag to run this model. -### Training results +## About us - - -### Framework versions - -- Transformers 4.22.0.dev0 -- TensorFlow 2.9.2 -- Datasets 2.4.0 -- Tokenizers 0.12.1 +This model was created by the team at [Impira](https://www.impira.com/).