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
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.
Vaccines are unsafe The coronavirus vaccine is either unsafe or part of a larger plot to control people or reduce the population.
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.
The Chinese intentionally spread the virus The Chinese government intentionally created or spread the coronavirus to harm other countries.
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.
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.
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
Direct Use
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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.
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
- hashtags, mentions, punctuation.
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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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
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- 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
Citation [optional]
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APA:
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Glossary [optional]
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Model Card Authors [optional]
@ikrysinska, @wtomi