c4-benchfilter-nano / README.md
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metadata
language_creators:
  - found
language:
  - en
license: odc-by
source_datasets:
  - c4
task_categories:
  - text-generation
  - fill-mask
task_ids:
  - language-modeling
  - masked-language-modeling
dataset_info:
  features:
    - name: text
      dtype: string
    - name: score
      dtype: float64
  splits:
    - name: train
      num_bytes: 373897649.51453334
      num_examples: 278115
  download_size: 242478448
  dataset_size: 373897649.51453334
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
size_categories:
  - 100K<n<1M

crumb/c4-benchfilter-nano

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.

The estimated top 10% of highest estimated length normalized ngram (mean of tri, quad, and penta-gram) overlaps for each of the selected benchmark datasets (arc, truthful_qa, hellaswag, mmlu, humaneval) based on 1k samples, within the first 3M samples of C4. The top scoring sample datasets for each benchmark are then filtered again for top 30% scores and combined and exact-match de-duplicated. Then the top 3% scores and samples less than 200 characters long are removed because they likely have exact large n-token matches by chance such as exact dates or times that aren't actually relevant to the data.*

*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.