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--- |
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language_creators: |
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- found |
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language: |
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- en |
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license: odc-by |
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source_datasets: |
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- c4 |
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task_categories: |
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- text-generation |
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- fill-mask |
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task_ids: |
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- language-modeling |
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- masked-language-modeling |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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- name: score |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 373897649.51453334 |
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num_examples: 278115 |
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download_size: 242478448 |
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dataset_size: 373897649.51453334 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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size_categories: |
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- 100K<n<1M |
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--- |
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# crumb/c4-benchfilter-nano |
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A 278k sample derivation of the first 3M samples from the C4 dataset for a cheap and short continued pretraining for language models to optimize for benchmark scores without sacrificing generalization and generative modelling unrelated to chat or 'instruct' data. |
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The estimated top 10% of highest estimated length normalized ngram (mean of tri, quad, and penta-gram) overlaps for each of the |
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selected benchmark datasets (arc, truthful_qa, hellaswag, mmlu, humaneval) based |
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on 1k samples, within the first 3M samples of C4. The top scoring sample |
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datasets for each benchmark are then filtered again for top 30% scores and |
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combined and exact-match de-duplicated. Then the top 3% scores and samples less than 200 characters long are removed |
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because they likely have exact large n-token matches by chance such as exact |
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dates or times that aren't actually relevant to the data.\* |
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\*Upon further examination, some of these samples are still present throughout the data, albeit at much lower frequency than before, you might benefit from using `dataset.filter(x['score'] > thresh)` for some threshold, but you risk losing high quality samples as well, this tradeoff should be well-examined before training. |
