---
license: mit
language:
- en
pipeline_tag: text-classification
arxiv: 2305.03695
---

# Model Card for Vera

<!-- Provide a quick summary of what the model is/does. -->

Vera is a commonsense statement verification model. See our paper at: <https://arxiv.org/abs/2305.03695>.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Jiacheng Liu, Wenya Wang, Dianzhuo Wang, Noah A. Smith, Yejin Choi, Hannaneh Hajishirzi
- **Shared by [optional]:** Jiacheng Liu
- **Model type:** Transformers
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** T5-v1.1-XXL

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** <https://github.com/liujch1998/vera> (Coming soon!)
- **Paper [optional]:** <https://arxiv.org/abs/2305.03695>
- **Demo [optional]:** <https://huggingface.co/spaces/liujch1998/vera>

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

Vera is intended to predict the correctness of commonsense statements.

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

Vera can be used to detect commonsense errors made by generative LMs (e.g., ChatGPT), or filter noisy commonsense knowledge generated by other LMs (e.g., Rainier).

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

Vera is a research prototype and may make mistakes. Do not use for making critical decisions. It is intended to predict the correctness of commonsense statements, and may be unreliable when taking input out of this scope.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

See the **Limitations and Ethics Statement** section of our paper.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

Please refer to <https://huggingface.co/spaces/liujch1998/vera/blob/main/app.py#L27-L98>

## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

See the **Data Construction** section of our paper.

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

See the **Model Training** section of our paper.

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

See the **Evaluation Results** section of our paper.

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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:** V100
- **Hours used:** 2560
- **Cloud Provider:** Private
- **Compute Region:** US
- **Carbon Emitted:** 331 kg

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

Jiacheng Liu