---
language: en
license: cc-by-4.0
datasets:
- multi_nli
library_name: transformers
pipeline_tag: text-classification
---

# Model Card for Model COVID-19-CT-tweets-classification

### Model Description

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This is a deBERTa-v3-base model with an adapter trained on X tweets from [More Information Needed] finetuned for text classification. The model predicts whether a tweet supports a given conspiracy theory or not. The model was trained on tweets related to six common COVID-19 conspiracy theories.

1. **Vaccines are unsafe** The coronavirus vaccine is either unsafe or part of a larger plot to control people or reduce the population. 

2. **Governments and politicians spread misinformation** Politicians or government agencies are intentionally spreading false information, or they have some other motive for the way they are responding to the coronavirus. 

3. **The Chinese intentionally spread the virus** The Chinese government intentionally created or spread the coronavirus to harm other countries. 

4. **Deliberate strategy to create economic instability or benefit large corporations** The coronavirus or the government's response to it is a deliberate strategy to create economic instability or to benefit large corporations over small businesses. 

5. **Public intentionally misled about the true nature of the virus and prevention** The public is being intentionally misled about the true nature of the Coronavirus, its risks, or the efficacy of certain treatments or prevention methods. 

6. **Human made and bioweapon** The Coronavirus was created intentionally, made by humans, or as a bioweapon. 


This model is suitable for English.

- **Developed by:** Webimmunication Team
- **Shared by [optional]:** @ikrysinska
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** EN
- **License:** CC BY 4.0
- **Finetuned from model [optional]:** https://huggingface.co./sileod/deberta-v3-base-tasksource-nli 

### Model Sources

- **Paper:** [More Information Needed]

- ## Uses

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### Direct Use

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[More Information Needed]

### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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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.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

- hashtags, mentions, punctuation.


#### 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]

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[More Information Needed]

## Evaluation

The model was evaluated on a sample 

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## 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:** GPU Tesla V100
- **Hours used:** 40
- **Cloud Provider:** Google Cloud Platform
- **Compute Region:** us-east1
- **Carbon Emitted:** 4.44 kg CO2 ([equivalent to: 17.9 km driven by an average ICE car, 2.22 kgs of coal burned, 0.07 tree seedlings sequesting carbon for 10 years](https://www.epa.gov/energy/greenhouse-gases-equivalencies-calculator-calculations-and-references)


## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

@ikrysinska, @wtomi

## Model Card Contact

izabela.krysinska@doctorate.put.poznan.pl
tomi.wojtowicz@doctorate.put.poznan.pl
mikolaj.morzy@put.poznan.pl