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language: en
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# 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](https://huggingface.co/microsoft/layoutlm-base-uncased)
- **Resources for more information:** 
  - [GitHub Repo](https://aka.ms/layoutlm)
  - [LayoutLM Associated Paper](https://arxiv.org/abs/1912.13318)
 	
 
# Uses
 
 
## Direct Use
 
This model can be used for the task of document image understanding.
The [LayoutLM model card](https://huggingface.co/microsoft/layoutlm-base-uncased) 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)](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>