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README.md
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@@ -60,13 +60,14 @@ The training proceeded in two steps. First, the model was trained on a weakly an
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The weak annotation was performed using GPT-4o. The prompt for labeling the data can be found [here](https://huggingface.co/Sami92/XLM-R-Large-ClaimDetection/blob/main/FactualityPrompt_GPT.txt). The data was taken from Telegram. More specifically from a set of about 200 channels that have been subject to a fact-check from either Correctiv, dpa, Faktenfuchs or AFP. The test data consists of 149 Telegram posts. The performance is as follows.
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| | precision | recall | f1-score | support |
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| **factual** |
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| **non-factual**|
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| **accuracy** | | |
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| **macro avg** |
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| **weighted avg** | 0.90 |
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The weak annotation was performed using GPT-4o. The prompt for labeling the data can be found [here](https://huggingface.co/Sami92/XLM-R-Large-ClaimDetection/blob/main/FactualityPrompt_GPT.txt). The data was taken from Telegram. More specifically from a set of about 200 channels that have been subject to a fact-check from either Correctiv, dpa, Faktenfuchs or AFP. The test data consists of 149 Telegram posts. The performance is as follows.
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| | precision | recall | f1-score | support |
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| **factual** | 0.88 | 0.92 | 0.90 | 71 |
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| **non-factual**| 0.92 | 0.88 | 0.90 | 78 |
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| **accuracy** | | | 0.90 | 149 |
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| **macro avg** | 0.90 | 0.90 | 0.90 | 149 |
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| **weighted avg** | 0.90 | 0.90 | 0.90 | 149 |
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