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---
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
- **Related Models:**
- **Parent Model:** LayoutLM
- **Resources for more information:**
- [GitHub Repo](https://aka.ms/layoutlm)
# Uses
## Direct Use
This model can be used for the task of Token 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 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](https://huggingface.co/microsoft/layoutlm-base-uncased) 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
More information needed
### Factors
### 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **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: 4.6.0
# 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]
More information needed
# More Information [optional]
More information needed
# Model Card Authors [optional]
Allen Institute for AI [allenai] 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.
<details>
<summary> Click to expand </summary>
```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")
```
</details>
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