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---
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: 406516813.06260204
num_examples: 302378
download_size: 245347331
dataset_size: 406516813.06260204
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# crumb/c4-benchfilter-nano
A derivation of the first 3M samples from the C4 dataset.
The estimated top 10% of highest n-token (mean 3,4,5) 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 20 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, 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. Another option is filtering out the shorter samples because they seem to be more likely to contain the exact string-matches and don't contribute to the data mixture as much anyway. |