readme: 
  ---
  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>
   
   
   
  
  