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--- |
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license: mit |
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configs: |
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- config_name: default |
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data_files: |
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- split: ratio1_v1 |
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path: ratio1_v1.csv |
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- split: ratio1_v2 |
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path: ratio1_v2.csv |
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- split: ratio2_v1 |
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path: ratio2_v2.csv |
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- split: ratio3_v1 |
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path: ratio3_v2.csv |
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- split: ratio6_v2 |
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path: ratio6_v2.csv |
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- split: ratio10_v1 |
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path: ratio10_v1.csv |
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- split: ratio30_v1 |
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path: ratio30_v1.csv |
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- split: ratio50_v1 |
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path: ratio50_v1.csv |
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- split: test |
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path: test.csv |
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--- |
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This dataset is composed of Claude-labelled [fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb/) documents. |
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For each document, Claude is asked if it is 'forecastable' (i.e. would be a reasonable seed for a pastcasting question) and to estimate the date the document was published. |
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V1 splits were generated by having Claude label ~50K random fineweb documents and v2 splits were augmented with labels on ~30K additional documents that a DebertaV3 classifier finetuned on ratio10_v1 thought were forecastable (Claude thought ~1/3 of these additional documents were forecastable). |
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Splits are of the form `ratio{negative_to_positive_ratio}_v{1 or 2}`. For example `ratio6_v2` is ~6 negative examples for each positive example. Splits do overlap. |
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`ratio6_v2` was used to train https://huggingface.co/noanabeshima/forecastability-classifier-v1. |
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Prompt can be found in `prompt.txt`. It was iterated on slightly using a small set of ground-truth human labels. GPT-4.1 performed slightly better, but Claude had a more favorable TOS for open-sourcing data/models. |
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Claude considers ~2% of fineweb documents to be forecastable. |
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Made with the help of Collin Gray. |
