---
license: apache-2.0
datasets:
- jigsaw_toxicity_pred
language:
- en
metrics:
- perplexity
---
# Model Card for `gminus`

This model is a `facebook/bart-large` fine-tuned on toxic comments from `jigsaw_toxicity_pred` dataset.

## Model Details

This model is not intended to be used for plain inference as it is very likely to predict toxic content.
It is intended to be used instead as "utility model" for detecting and fixing toxic content as its token probability distributions will likely differ from comparable models not trained/fine-tuned over toxic data.
Its name `gminus` refers to the _G-_ model in [Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts](https://aclanthology.org/2023.acl-short.21.pdf).

### Model Description


- **Developed by:** [tteofili]
- **Shared by :** [tteofili]
<!--- **Model type:** [More Information Needed]-->
<!--- **Language(s) (NLP):** [More Information Needed]-->
- **License:** [apache-2.0]
- **Finetuned from model :** [facebook/bart-large](https://huggingface.co/facebook/bart-large)
<!--
### Model Sources [optional]

Provide the basic links for the model.

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
-->

## 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.

[More Information Needed]

### Downstream Use [optional]

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

[More Information Needed]

### Out-of-Scope Use

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

[More Information Needed]
-->
## Bias, Risks, and Limitations

This model is fine-tuned over toxic comments from `jigsaw_toxicity_pred` and it is very likely to produce toxic content.
For this reason this model should only be used in combination with other models for the sake of detecting / fixing toxic content, see for example [Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts](https://aclanthology.org/2023.acl-short.21.pdf).

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

[More Information Needed]

### 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.

[More Information Needed]

## 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. 

[More Information Needed]

### Training Procedure 

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

#### 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. 

### Testing Data, Factors & Metrics

#### Testing Data

 This model was tested on `jigsaw_toxic_pred` testset.

<!-- 
#### Factors

 These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. 

[More Information Needed]
-->
#### Metrics

 Model was evaluated using `perplexity` (on the MLM task).

### Results

Perplexity: _1.03_

<!--
#### 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:** [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

[More Information Needed]

## 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:**

[More Information Needed]

**APA:**

[More Information Needed]

## 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

[More Information Needed]