| --- |
| license: odc-by |
| pretty_name: General · Web · Italian · 2026-08 |
| task_categories: |
| - text-generation |
| language: |
| - it |
| size_categories: |
| - 10M<n<100M |
| tags: |
| - pretraining |
| - corpus |
| - italian |
| - web |
| - fineweb2 |
| dataset_info: |
| - config_name: fineweb2-hq-ita_Latn |
| features: |
| - name: doc_id |
| dtype: int64 |
| - name: source_id |
| dtype: string |
| - name: text |
| dtype: string |
| - name: date |
| dtype: string |
| - name: dump |
| dtype: string |
| - name: language |
| dtype: string |
| - name: language_score |
| dtype: float64 |
| - name: language_script |
| dtype: string |
| - name: minhash_cluster_size |
| dtype: int64 |
| - name: top_langs |
| dtype: string |
| - name: url |
| dtype: string |
| - name: quality_score |
| dtype: float64 |
| - name: uniq_ratio |
| dtype: float32 |
| splits: |
| - name: train |
| num_bytes: 71759847958 |
| num_examples: 21065052 |
| download_size: 24085760632 |
| dataset_size: 71759847958 |
| configs: |
| - config_name: fineweb2-hq-ita_Latn |
| data_files: |
| - split: train |
| path: curated/fineweb2-hq-ita_Latn/*.parquet |
| --- |
| |
| # General · Web · Italian · 2026-08 |
|
|
| Italian pretraining text, built from the Italian portion of EPFL's FineWeb2-HQ, which is the |
| high quality slice of FineWeb-2. Every document passes one character-level cleaner and a |
| repetition filter. |
|
|
| 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 |
|
|
| Pretraining or continuation pretraining of an Italian or multilingual language model, with 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 selection is inherited whole and unexamined. `quality_score` is kept |
| but nothing is filtered on it. |
|
|
| The `uniq_ratio` filter above removes a document that repeats itself. It never compares one |
| document against another, so it is not a deduplication step. No cross-document deduplication |
| was applied here, beyond what FineWeb-2 did upstream, and `minhash_cluster_size` is kept so a |
| later stage can dedupe further. |
|
|
| No filter for personal information has been applied. Italian only, and the language label |
| comes from the upstream classifier rather than from an independent check. |
|
|
