William Jeynes
25 lines
2.0 KiB
Markdown
25 lines
2.0 KiB
Markdown
# 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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| Original (RAGAS) | 56.01 | 57.73 |
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| Roberta (base) | 75 | 70 |
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| Roberta (Generated Data) | 76 | 71 |
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| Roberta (Generated Data + Back Translation) | 74 | 71 |
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| Roberta (Generated Data + Back Translation + Thresholding) | 77 | 90 |Y|[Here](https://huggingface.co/WillJeynes/LLMsForDisinformationAnalysis)
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| Distilled Roberta | 72.73 | 69.57 |
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| Flan | 79.17 | 85.71 |Y|[Here](https://huggingface.co/WillJeynes/LLMsForDisinformationAnalysis-Flan)
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| Simple Regression Model | 74.77 | 85.71 |Y|[Here](https://huggingface.co/WillJeynes/LLMsForDisinformationAnalysis-Regression)
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| Ensemble Model (weighted confidence score sum) | 84.21 | 83.33 |
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| Ensemble Model (majority voting) | 80.2 | 95.12 | |