Update documentation. Stop storing context. Decide on final claims source
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# Classifier work for evaluating model quality
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Made using a dataset of 1000 labeled claims from MVP pipeline.
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Roberta model trained on an augmented dataset with LLM generated adversarial examples for low frequency labels.
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Flan model trained using raw labelled claims, inherrent natural language ability allows for pattern recognition without the need for fake data.
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Regression model trained using the roberta dataset.
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Used ensemble model in the final version, with the component models available on Hugging Face.
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| Model | % Correct | % Valid taken forward|Used in ensemble|Link
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|------------------------------------------------------------|-----------|----------------------|----------------|-
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| Original | 53.22 | 61.72 |
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