metadata

language: en

Model Card for ivila-row-layoutlm-finetuned-s2vl-v2

Model Details

Model Description

Developed by: Allen Institute for AI [allenai]
Shared by [Optional]: More information needed
Model type: Token Classification
Language(s) (NLP): en
License: More information needed
Parent Model: LayoutLM
Resources for more information:
- GitHub Repo
- LayoutLM Associated Paper

Uses

Direct Use

This model can be used for the task of document image understanding. The LayoutLM model card notes:

LayoutLM is a simple but effective pre-training method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. LayoutLM archives the SOTA results on multiple datasets.

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

See the LayoutLM model card for more information

LayoutLM was pre-trained on IIT-CDIP Test Collection 1.0* dataset with two settings. LayoutLM-Base, Uncased (11M documents, 2 epochs): 12-layer, 768-hidden, 12-heads, 113M parameters (This Model) LayoutLM-Large, Uncased (11M documents, 2 epochs): 24-layer, 1024-hidden, 16-heads, 343M parameters

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

Metrics

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Results

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Model Examination

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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

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Hardware

More information needed

Software

Transformers_version: 4.6.0

Citation

BibTeX:

@misc{xu2019layoutlm,
   title={LayoutLM: Pre-training of Text and Layout for Document Image Understanding},
   author={Yiheng Xu and Minghao Li and Lei Cui and Shaohan Huang and Furu Wei and Ming Zhou},
   year={2019},
   eprint={1912.13318},
   archivePrefix={arXiv},
   primaryClass={cs.CL}
}

Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

Allen Institute for AI [allenai] in collaboration with Ezi Ozoani and the Hugging Face team

Model Card Contact

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How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

```python from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("allenai/ivila-row-layoutlm-finetuned-s2vl-v2")

model = AutoModelForTokenClassification.from_pretrained("allenai/ivila-row-layoutlm-finetuned-s2vl-v2")

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