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--- |
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language: en |
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license: cc-by-4.0 |
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datasets: |
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- multi_nli |
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library_name: transformers |
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pipeline_tag: text-classification |
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--- |
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# Model Card for Model COVID-19-CT-tweets-classification |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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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. |
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1. **Vaccines are unsafe** The coronavirus vaccine is either unsafe or part of a larger plot to control people or reduce the population. |
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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. |
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3. **The Chinese intentionally spread the virus** The Chinese government intentionally created or spread the coronavirus to harm other countries. |
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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. |
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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. |
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6. **Human made and bioweapon** The Coronavirus was created intentionally, made by humans, or as a bioweapon. |
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This model is suitable for English. |
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- **Developed by:** Webimmunication Team |
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- **Shared by [optional]:** @ikrysinska |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** EN |
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- **License:** CC BY 4.0 |
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- **Finetuned from model [optional]:** https://huggingface.co./sileod/deberta-v3-base-tasksource-nli |
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### Model Sources |
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- **Paper:** [More Information Needed] |
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- ## Uses |
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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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## Bias, Risks, and Limitations |
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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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- hashtags, mentions, punctuation. |
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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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## Evaluation |
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The model was evaluated on a sample |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Data Card if possible. --> |
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[More Information Needed] |
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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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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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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:** GPU Tesla V100 |
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- **Hours used:** 40 |
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- **Cloud Provider:** Google Cloud Platform |
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- **Compute Region:** us-east1 |
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- **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) |
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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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@ikrysinska, @wtomi |
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## Model Card Contact |
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[email protected] |
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[email protected] |
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[email protected] |
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