Brainquiver/general-web-it-202608

General · Web · Italian · 2026-08

Italian pretraining text, cleaned and filtered for the AURI family of small language models. It is the Italian half of a two-language continuation corpus that follows an English base. It is built from the Italian portion of EPFL's FineWeb2-HQ, which is the high quality slice of FineWeb-2.

21,065,052 documents and 66,158,573,443 characters of Italian prose.

Contents

Config	Documents	Characters	Upstream
fineweb2-hq-ita_Latn	21,065,052	66,158,573,443	epfml/FineWeb2-HQ, ita_Latn

The character count is exact. The token count is not stated, because a token count is a property of a tokenizer and not of the text: the same characters give a different count at every vocabulary size. As a rough guide, 4.3 characters per token puts this corpus near 15.4B tokens, and the real figure follows once the vocabulary is fixed.

Format

One row is one document. text holds the cleaned prose and every other column is provenance or a measurement.

Column	Type	Meaning
doc_id	int64	Position of the document in the upstream shard order
source_id	string	The upstream CommonCrawl record id, unchanged
text	string	The cleaned document
date	string	Crawl date of the record
dump	string	CommonCrawl dump that held the record
language	string	Upstream language label, ita throughout
language_score	float64	Upstream confidence in that label
language_script	string	Upstream script label, Latn throughout
minhash_cluster_size	int64	Size of the upstream near-duplicate cluster of the document
top_langs	string	Upstream per-language confidence, as JSON
url	string	Page the document came from
quality_score	float64	Upstream FineWeb2-HQ classifier score
uniq_ratio	float32	Distinct non-blank lines over non-blank lines, computed by us

doc_id counts the upstream row and not the kept row. It is therefore a coordinate into the upstream shard. It stays stable when a filter removes rows, so the sequence has gaps where rows went. It runs from 0 to 21,180,301 across 21,065,052 rows.

Two upstream columns are dropped. embeddings is 74.68 percent of every upstream file and holds the vectors that the quality classifier produced, which a language model does not read. file_path is an S3 path to a CommonCrawl record, and dump with url already say where a document came from.

Cleaning

Every document passes one character-level cleaner, which applies the Brainquiver text cleaning specification in this order:

1. Repair mojibake, meaning text that was decoded in the wrong encoding once already.
2. Normalise to NFC. Not NFKC, which would fold distinctions that carry meaning, such as the difference between a superscript digit and a digit.
3. Fold every line break convention to a single line feed.
4. Apply the substitution tables: typographic quotes and dashes become their keyboard equivalents, ligatures expand, and invisible formatting characters go.
5. Delete controls and unassigned code points.
6. Collapse whitespace runs. A run of two or more line breaks becomes one blank line. A run of one line break stays one line break. A run of spaces becomes one space.
7. Trim.

Cleaning changed the number of distinct code points from 21,136 to 20,971. 165 code points left and none arrived, because Italian web text uses few of the characters that step 4 expands into several others.

Character count rose slightly under cleaning, from 66,695,924,258 to 66,702,314,366 across all rows before filtering. That is expected: a ligature that becomes two letters, and a typographic ellipsis that becomes three periods, both add characters.

The uniq_ratio filter

uniq_ratio is the count of distinct non-blank lines over the count of non-blank lines. A document of wholly distinct lines scores 1.0, and a document that repeats a navigation block or a comment template scores lower. Every row below 0.80 is removed.

Measure	Value
Threshold	0.80
Upstream rows	21,180,304
Rows kept	21,065,052
Rows removed	115,252, or 0.544 percent
Mean uniq_ratio kept	0.9946
1st percentile kept	0.8571
Rows at exactly 1.0	19,676,007, or 93.4 percent

The removed rows are not published. They are recoverable exactly, because doc_id is an upstream coordinate: the set of removed rows is the difference between the full upstream doc_id range and the range present here.

The rate is low because FineWeb-2 already applies Gopher-style repetition filtering upstream, so this filter catches only what survived that pass.

Provenance

Built from epfml/FineWeb2-HQ, config ita_Latn, revision c0c06e94fd3a44ae9e802b2b0fc533817601eb5e, downloaded on 9 August 2026. That release is the high quality slice of FineWeb-2, selected by a classifier that the EPFL group trained. Nothing here re-crawls the web, and no document is rewritten: the only changes are the character-level cleaning above and the removal of rows below the threshold.

Upstream is ODC-By 1.0, and this corpus keeps that licence. FineWeb-2 derives from CommonCrawl, so a downstream user should also respect the CommonCrawl terms of use.

Intended use

Continuation pretraining of a small Italian or multilingual language model, after an English base. It suits a causal language modelling objective on text alone. The other columns let a later stage filter harder, weight a sample, or trace a document back to its page. They are not meant to be part of the training text.

Limitations

Web text, so it carries the biases, errors and dated statements of the pages it came from. The upstream quality classifier selects for a particular idea of quality, and that selection is inherited whole and unexamined. quality_score is kept but nothing is filtered on it. No deduplication beyond what FineWeb-2 applied upstream, and minhash_cluster_size is kept so a later stage can dedupe further. No personal information filtering has been applied. Italian only, and the language label comes from the upstream classifier rather than from an independent check.