metadata
dataset_info:
features:
- name: id
dtype: int64
- name: text
dtype: string
- name: meta
struct:
- name: warc_headers
struct:
- name: warc-record-id
dtype: string
- name: warc-date
dtype: string
- name: content-type
dtype: string
- name: content-length
dtype: int32
- name: warc-type
dtype: string
- name: warc-identified-content-language
dtype: string
- name: warc-refers-to
dtype: string
- name: warc-target-uri
dtype: string
- name: warc-block-digest
dtype: string
- name: identification
struct:
- name: label
dtype: string
- name: prob
dtype: float32
- name: annotations
sequence: string
- name: line_identifications
list:
- name: label
dtype: string
- name: prob
dtype: float32
- name: perplexity_score
dtype: float64
- name: text_length
dtype: int64
- name: url
dtype: string
- name: domain
dtype: string
- name: dup_ratio
dtype: float64
- name: pairs
sequence:
sequence: int64
- name: repetitions
sequence: binary
- name: included_in_dedup
dtype: bool
- name: cluster
sequence: int64
- name: has_dup_25
dtype: bool
splits:
- name: train
num_bytes: 3188540880787
num_examples: 431992659
download_size: 1732364041898
dataset_size: 3188540880787
Use the 25% suffix array to deduplicate the full Oscar, i.e. remove any document which has an at least 100-char span overlapping with the 25% chunk we selected in the previous bullet. This is more permissive and leaves us with 136 million documents or 31% of the original dataset. Also for reasons the explanation of which would probably involve terms like power laws, we still remove most of the most pervasive duplicates - so I'm pretty optimistic about this being useful.