Model Card for LayoutLM for Document Classification

Model Details

Model Description

This is a fine-tuned version of the multi-modal LayoutLM model for the task of classification on documents.

• Developed by: Impira team
• Shared by [Optional]: 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

Downstream Use [Optional]

More information needed

Out-of-Scope Use

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

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

Training Data

More information needed

Training Procedure

More information needed

Preprocessing

More information needed

Speeds, Sizes, Times

Num_attention_head: 12 Num_hidden_layer:12, Vocab_size: 30522

Evaluation

Testing Data, Factors & Metrics

Testing Data

More information needed

Factors

More information needed

Metrics

More information needed

Results

More information needed

Model Examination

More information needed

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019) .

• Hardware Type: More information needed
• Hours used: More information needed
• Cloud Provider: More information needed
• Compute Region: More information needed
• Carbon Emitted: More information needed

Technical Specifications [optional]

Model Architecture and Objective

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

Transformers version: 4.4.0.dev0

Citation

BibTeX:

More information needed}

APA:

More information needed

Glossary [optional]

More information needed

More Information [optional]

More information needed

Model Card Authors [optional]

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

Model Card Contact

More information needed

How to Get Started with the Model

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")