Curated English Bluesky Corpus

Dataset summary

This dataset is a large English-only Bluesky text corpus curated for tokenizer and language-model experiments.

The corpus was built in two stages:

1. Stage 1: a full download of the Hugging Face dataset Roronotalt/bluesky, filtered to English posts and deduplicated by post URI.
2. Stage 2: additional English Bluesky posts collected directly from Bluesky repositories using a custom extractor, again deduplicated by URI and checked against Stage 1 to avoid overlap.

The final merged corpus contains 90,831,330 rows and 1,500,002,598 whitespace-counted words.

Only two columns are kept in the released dataset:

• uri: the Bluesky AT URI of the post
• text: the post text

Why this dataset was created

This corpus was curated to obtain a large English social-media fitting corpus with a target size of roughly 1.5 billion words, suitable for tokenizer training and related downstream experiments.

Data fields

• uri (string): unique post identifier in AT URI format.
• text (string): raw post text.

Dataset splits

The final release is stored as a DatasetDict with three splits:

• train: 89,062,186 rows
• validation: 891,793 rows
• test: 877,351 rows

Split creation was based on cumulative whitespace word counts, not random row sampling:

• validation was filled first until it reached approximately 1% of total words,
• test was filled next until it reached approximately 1% of total words,
• train contains the remainder.

Final split word counts are:

• train: 1,470,002,529 words
• validation: 15,000,031 words
• test: 15,000,038 words

Curation pipeline

Stage 0: source download

The initial source used for Stage 1 was the full train split of Roronotalt/bluesky, downloaded from Hugging Face in non-streaming mode and saved locally before filtering.

Stage 1: filtering and deduplication of the Hugging Face source

The downloaded source was processed row by row with the following rules:

1. Keep a row only if its langs metadata contains en or a value starting with en-.
2. Drop rows with missing or empty uri.
3. Drop rows with missing or empty text.
4. Count words using whitespace splitting and drop rows with fewer than 1 word.
5. Deduplicate by uri.
6. Keep only the columns uri and text.

Stage 1 statistics:

• Processed source samples: 94,967,071
• English rows detected: 60,854,982
• Rows dropped for missing URI: 0
• Rows dropped for missing text: 2,305,001
• Rows dropped for fewer than 1 word: 2,640
• Rows dropped as duplicate URI: 0
• Final Stage 1 rows kept: 58,547,341
• Final Stage 1 words kept: 966,013,061

Stage 2: direct Bluesky extraction to extend the corpus

After Stage 1, a second collection pass was run to add more English Bluesky posts until the corpus reached the target scale.

This step used a custom Go extractor with the following logic:

1. Export all Stage 1 URIs into a lookup file.
2. Query the Bluesky relay at https://bsky.network using SyncListReposByCollection("app.bsky.feed.post") to discover repositories containing posts.
3. For each discovered DID, resolve its PDS endpoint.
4. Fetch the repository snapshot with SyncGetRepo.
5. Stream repository records and keep only records whose path starts with app.bsky.feed.post/.
6. Read post text and langs metadata from each record.
7. Apply the same English rule as in Stage 1: keep posts only if langs contains en or a tag starting with en-.
8. Drop posts with missing text, missing URI, or fewer than 1 whitespace-counted word.
9. Drop posts whose URI already appeared in Stage 1.
10. Deduplicate within Stage 2 by URI.
11. Stop collection once the additional corpus exceeded the target number of new words needed to bring the merged corpus to roughly 1.5B words.

Stage 2 statistics:

• Stage 1 URIs exported for duplicate checking: 58,547,341
• Repositories processed: 8,977
• Posts seen: 51,955,104
• English posts seen: 33,219,791
• Dropped for missing text: 2,611,525
• Dropped for non-English: 16,123,788
• Dropped for fewer than 1 word: 13,837
• Dropped for missing URI: 0
• Dropped as duplicate with Stage 1: 921,965
• Dropped as duplicate within Stage 2: 0
• Final Stage 2 rows kept: 32,283,989
• Final Stage 2 words kept: 533,989,535

Final merge and split materialization

The Stage 1 and Stage 2 datasets were concatenated, then deduplicated once more by uri during the final merge step. No additional duplicate URIs were found at this stage.

Final merge statistics:

• Rows loaded from Stage 1: 58,547,341
• Rows loaded from Stage 2: 32,283,989
• Rows after concatenation: 90,831,330
• Rows dropped for empty URI during final merge: 0
• Rows dropped for empty text during final merge: 0
• Rows dropped as duplicate URI during final merge: 0
• Final total rows: 90,831,330
• Final total words: 1,500,002,598

Curation rules at a glance

• Language filter: keep only rows whose language metadata contains en or an en-* variant.
• Minimum length: at least 1 whitespace-counted word.
• Deduplication: URI-based.
• Released columns: uri, text.
• Word counting: whitespace-based (split() / strings.Fields).

Important notes and limitations

1. English detection relies on metadata, not an external language identification model. In both stages, English selection was based on the langs field attached to the source record.
2. Deduplication is URI-based, not text-based. Near-duplicates or repeated text with different URIs may still remain.
3. The validation and test splits are convenience splits. They were created by sequential cumulative word count, not by time-based, user-based, or author-disjoint partitioning.
4. Stage 2 extraction skipped unavailable repositories. During direct extraction, some repositories were unavailable, deactivated, taken down, not found, or timed out. These failures were logged and skipped rather than retried indefinitely.
5. The corpus is intended primarily as a large-scale text resource. It is well suited for tokenizer fitting and corpus-level experimentation, but the provided validation/test splits should not automatically be treated as a benchmark design for all downstream tasks.

Recommended use

This dataset is especially appropriate for:

• tokenizer training,
• corpus statistics,
• vocabulary analysis,
• language-model pretraining or continued pretraining experiments,
• studies of social-media language variation.

Source attribution

Stage 1 was derived from the Hugging Face dataset Roronotalt/bluesky.

Stage 2 was collected directly from Bluesky repositories through a custom extraction pipeline over Bluesky infrastructure, with the same English filtering and URI-level deduplication rules applied.

Acknowledgement

If you use this dataset, please acknowledge both:

1. the original Roronotalt/bluesky source used for Stage 1, and
2. this curated release, which adds English filtering, URI-based deduplication, direct Bluesky augmentation, and train/validation/test split materialization.