Layoutlm-document-classifier Open-source Document Classification Model - Free Deployment for Precise Processing of PDF Invoice Classification

Layoutlm Document Classifier

Developed by impira

A document classification model fine-tuned based on the LayoutLM architecture, specifically designed for classifying PDF documents, especially invoices

Multimodal Fusion

Transformers

English#Multimodal Document Classification #Invoice Recognition #PDF Text Analysis

Downloads 64

Release Time : 9/20/2022

Model Overview

This is a multimodal LayoutLM model fine-tuned for document classification tasks, particularly adept at classifying structured documents like invoices

Model Features

Multimodal Processing Capability

Combines text content and layout information for document classification

Specialized in Invoice Classification

Specially optimized for invoice documents

Structured Document Processing

Excels at processing structured document formats like PDF

Model Capabilities

Document Classification

PDF Processing

Invoice Recognition

Use Cases

Financial Automation

Automatic Invoice Classification

Automatically identifies and classifies different types of invoice documents

Document Management

Enterprise Document Archiving

Automatically classifies enterprise documents based on content

🚀 Model Card for LayoutLM for Document Classification

This is a fine-tuned multi-modal LayoutLM model for document classification tasks, offering efficient and accurate text classification capabilities.

🚀 Quick Start

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-classifier")

model = AutoModelForSequenceClassification.from_pretrained("impira/layoutlm-document-classifier")

✨ Features

This is a fine-tuned version of the multi-modal LayoutLM model, specialized for document classification tasks.
It can be used for text classification.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-classifier")

model = AutoModelForSequenceClassification.from_pretrained("impira/layoutlm-document-classifier")

📚 Documentation

Model Details

Property	Details
Developed by	Impira team
Shared by	Hugging Face
Model Type	Text Classification
Language(s) (NLP)	en
License	cc-by-nc-sa-4.0
Related Models	layoutlm
Parent Model	More information needed
Resources for more information	Associated Paper, Blog Post

Uses

Direct Use

Text Classification

Out-of-Scope Use

⚠️ Important Note

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Recommendations

💡 Usage Tip

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Speeds, Sizes, Times

Property	Details
Num_attention_head	12
Num_hidden_layer	12
Vocab_size	30522

Technical Specifications

Software

Transformers version: 4.4.0.dev0

Citation

BibTeX: More information needed

APA: More information needed

Model Card Authors

Impira team in collaboration with Ezi Ozoani and the Hugging Face team.

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