readme: 
  ---
  language: en
  datasets:
  - cuad
  ---
  
  # Model Card for RoBERTa Large Model fine-tuned with CUAD dataset
   
  This model is the fine-tuned version of "RoBERTa Large" using CUAD dataset
   
   
   
   
   
  # Model Details
   
  ## Model Description
   
  The [Contract Understanding Atticus Dataset (CUAD)](https://www.atticusprojectai.org/cuad), pronounced "kwad", a dataset for legal contract review curated by the Atticus Project. 
   
  Contract review is a task about "finding needles in a haystack."
  We find that Transformer models have nascent performance on CUAD, but that this performance is strongly influenced by model design and training dataset size. Despite some promising results, there is still substantial room for improvement. As one of the only large, specialized NLP benchmarks annotated by experts, CUAD can serve as a challenging research benchmark for the broader NLP community. 
   
  - **Developed by:** TheAtticusProject
  - **Shared by [Optional]:** HuggingFace
  - **Model type:** Language model
  - **Language(s) (NLP):** en
  - **License:** More information needed
  - **Related Models:** RoBERTA
    - **Parent Model:**RoBERTA Large
  - **Resources for more information:**
  - [GitHub Repo](https://github.com/TheAtticusProject/cuad) 
  - [Associated Paper](https://arxiv.org/abs/2103.06268) 
   
  # Uses
   
  ## Direct Use
   
  Legal contract review
   
  ## 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 recomendations.
   
   
  # Training Details
   
  ## Training Data
  See [cuad dataset card](https://huggingface.co/datasets/cuad) for further details
   
  ## Training Procedure
   
  More information needed
   
  ### Preprocessing
   
  More information needed
   
  ### Speeds, Sizes, Times
   
  More information needed
   
  # Evaluation
   
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
  #### Extra Data
  Researchers may be interested in several gigabytes of unlabeled contract pretraining data, which is available [here](https://drive.google.com/file/d/1of37X0hAhECQ3BN_004D8gm6V88tgZaB/view?usp=sharing).
   
  ### Factors
   
  More information needed
   
  ### Metrics
   
  More information needed
   
  ## Results 
   
   
  
  
  We [provide checkpoints](https://zenodo.org/record/4599830) for three of the best models fine-tuned on CUAD: RoBERTa-base (~100M parameters), RoBERTa-large (~300M parameters), and DeBERTa-xlarge (~900M parameters).
   
  
   
   
  # 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
   
  The HuggingFace [Transformers](https://huggingface.co/transformers) library. It was tested with Python 3.8, PyTorch 1.7, and Transformers 4.3/4.4. 
   
  # Citation
   
   
  **BibTeX:**
   
   @article{hendrycks2021cuad,
        title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review}, 
        author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
        journal={NeurIPS},
        year={2021}
  }
   
   
   
  # Glossary [optional]
   
  More information needed
   
  # More Information [optional]
   
  For more details about CUAD and legal contract review, see the [Atticus Project website](https://www.atticusprojectai.org/cuad).
   
  # Model Card Authors [optional]
   
  TheAtticusProject
   
  # Model Card Contact
   
  [TheAtticusProject](https://www.atticusprojectai.org/), in collaboration with the Ezi Ozoani and the HuggingFace Team
   
   
  # 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, AutoModelForQuestionAnswering
   
  tokenizer = AutoTokenizer.from_pretrained("akdeniz27/roberta-large-cuad")
   
  model = AutoModelForQuestionAnswering.from_pretrained("akdeniz27/roberta-large-cuad")
  ```
  
   
  </details>
  
  