---
configs:
- config_name: bytelevel
  default: true
  data_files:
  - split: train
    path: bytelevel2/*.parquet
- config_name: bytelevel-llm-data
  data_files:
  - split: fw57M
    path: bytelevel-llm-data/fw57M/fw57M-*
  - split: ngram
    path: bytelevel-llm-data/ngram/ngram-*
- config_name: bytelevel-subset
  data_files:
  - split: train
    path: bytelevel-subset/train-*
- config_name: bytelevel-subset_1
  data_files:
  - split: train
    path: bytelevel-subset_1/train-*
- config_name: bytelevel-subset_2
  data_files:
  - split: train
    path: bytelevel-subset_2/train-*
- config_name: BPE_64000
  data_files:
  - split: train
    path: BPE_64000/*.parquet
- config_name: ByteSpanSurprisalCombinedFrequency_64000
  data_files:
  - split: train
    path: ByteSpanSurprisalCombinedFrequency_64000/*.parquet
- config_name: ByteSpanSurprisalMonotonicFrequency_64000
  data_files:
  - split: train
    path: ByteSpanSurprisalMonotonicFrequency_64000/*.parquet
- config_name: ByteSpanSurprisalMonotonicSeeding_64000
  data_files:
  - split: train
    path: ByteSpanSurprisalMonotonicSeeding_64000/*.parquet
- config_name: ByteSpanSurprisalCombinedSeeding_64000
  data_files:
  - split: train
    path: ByteSpanSurprisalCombinedSeeding_64000/*.parquet
- config_name: ByteSpanSurprisalGlobalIncrement_64000
  data_files:
  - split: train
    path: ByteSpanSurprisalGlobalIncrement_64000/*.parquet
- config_name: BPEWP_64000
  data_files:
  - split: train
    path: BPEWP_64000/*.parquet
language:
- en
tags:
- language modeling
pretty_name: FineWebEDU 20B
size_categories:
- 10B<n<100B
---

# FineWebEDU 20B

A copy of [FineWebEDU-20B](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) used for out tokenizer experiments. The subsets are as follows:
- `bytelevel`: the full dataset tokenized using our bytelevel tokenizer
- `bytelevel-subset_1`: a 100k-row subset of the bytelevel subset, used to train bytelevel models. 
- `bytelevel-subset_2`: a 100k-row subset of the bytelevel subset, used to extract llm predictions. 
- `bytelevel-llm-data`: a copy of `bytelevel-subset_2` with lm predictions, used to train bytespan tokenizers
- `bytelevel-subset_3`: a 100k-row subset of the bytelevel subset, used to evaluate trained tokenizers

The remaining subsets are all versions of the dataset tokenized with our trained tokenizers:
- `BPE_64000`
- `BPEWP_64000`
- `ByteSpanSurprisalMonotonicFrequency_64000`
- `ByteSpanSurprisalMonotonicSeeding_64000`
- `ByteSpanSurprisalCombinedFrequency_64000`
- `ByteSpanSurprisalCombinedSeeding_64000`
- `ByteSpanSurprisalGlobalIncrement_64000`