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---
language: en
license: cc-by-nc-sa-4.0
tags:
- layoutlm
- document-classification
- pdf
- invoices
---
# 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](https://arxiv.org/abs/1912.13318)
- [Blog Post](https://www.impira.com/blog/introducing-instant-invoices)
# 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)](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
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](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: 4.4.0.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.
<details>
<summary> Click to expand </summary>
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("impira/layoutlm-document-classifier")
model = AutoModelForSequenceClassification.from_pretrained("impira/layoutlm-document-classifier")
```
</details>
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