transformers safetensors bart text2text-generation en dataset:raquiba/Sarcasm_News_Headline arxiv:1910.09700 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
datasets:
- raquiba/Sarcasm_News_Headline
language:
- en
metrics:
- perplexity
---
# Model Card for `sarcasm_plus`

This model is a `facebook/bart-large` fine-tuned on sarcastic comments from `raquiba/Sarcasm_News_Headline` dataset.

## Model Details

This model is not intended to be used for plain inference as it is very likely to predict non-sarcastic content.
It is intended to be used instead as "utility model" for detecting and fixing sarcastic content as its token probability distributions will likely differ from comparable models not trained/fine-tuned over sarcastic data.
Its name `sarcasm_plus` 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]
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- **License:** [apache-2.0]
- **Finetuned from model :** [facebook/bart-large](https://huggingface.co/facebook/bart-large)
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## Uses

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## Bias, Risks, and Limitations

This model is fine-tuned over non-sarcastic comments from `raquiba/Sarcasm_News_Headline` and it is very likely to produce non-sarcastic content.
For this reason this model should only be used in combination with other models for the sake of detecting / fixing sarcastic 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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## How to Get Started with the Model

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## Training Details

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

 This model was tested on `raquiba/Sarcasm_News_Headline` testset.

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#### Metrics

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

### Results

Perplexity: _1.09_

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## Environmental Impact

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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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