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README.md
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license: apache-2.0
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---
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license: apache-2.0
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datasets:
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- jigsaw_toxicity_pred
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language:
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- en
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metrics:
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- perplexity
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---
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# Model Card for `gminus`
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This model is a `facebook/bart-large` fine-tuned on non-toxic comments from `jigsaw_toxicity_pred` dataset.
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Only a subset (20%) of the non-toxic comments were used for training this dataset.
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## Model Details
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This model is not intended to be used for plain inference, even though it is unlikely to predict toxic content.
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It is intended to be used as "utility model" for detecting and fixing toxic content as its token probability distributions will likely differ from comparable models trained/fine-tuned over toxic data.
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Its name `gplus` 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).
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### Model Description
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- **Developed by:** [tteofili]
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- **Shared by :** [tteofili]
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<!--- **Model type:** [More Information Needed]-->
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<!--- **Language(s) (NLP):** [More Information Needed]-->
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- **License:** [apache-2.0]
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- **Finetuned from model :** [facebook/bart-large](https://huggingface.co/facebook/bart-large)
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<!--
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### Model Sources [optional]
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Provide the basic links for the model.
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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-->
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## Uses
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<!--
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Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model.
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### Direct Use
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This section is for the model use without fine-tuning or plugging into a larger ecosystem/app.
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[More Information Needed]
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### Downstream Use [optional]
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This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app
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[More Information Needed]
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### Out-of-Scope Use
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This section addresses misuse, malicious use, and uses that the model will not work well for.
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[More Information Needed]
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-->
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## Bias, Risks, and Limitations
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This model is fine-tuned over non-toxic comments from `jigsaw_toxicity_pred`, it is unlikely to produce toxic content.
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Nevertheless, 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).
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<!--
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This section is meant to convey both technical and sociotechnical limitations.
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[More Information Needed]
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### Recommendations
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This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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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.
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[More Information Needed]
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### Training Procedure
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This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure.
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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**Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision
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#### Speeds, Sizes, Times [optional]
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- This section provides information about throughput, start/end time, checkpoint size if relevant, etc.
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[More Information Needed]
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-->
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## Evaluation
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This section describes the evaluation protocols and provides the results.
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### Testing Data, Factors & Metrics
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#### Testing Data
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This model was tested on `jigsaw_toxic_pred` testset.
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<!--
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#### Factors
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These are the things the evaluation is disaggregating by, e.g., subpopulations or domains.
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[More Information Needed]
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-->
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#### Metrics
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Model was evaluated using `perplexity` (on the MLM task).
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### Results
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Perplexity: _1.02_
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<!--
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#### Summary
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## Model Examination [optional]
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- Relevant interpretability work for the model goes here
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[More Information Needed]
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## Environmental Impact
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Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section.
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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If relevant, include terms and calculations in this section that can help readers understand the model or model card.
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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