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Datasets:
unparallelled
/
BluScriptCP-data

Tasks:
Text Generation
Languages:
English
Size:
10B<n<100B
Tags:
gpt2
tokenized
long-context
pretraining
License:
Dataset card Files
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  1. README.md +98 -0
  2. b1_4096_8192/part_80.meta.json +13 -0
  3. b2_8192_16384/part_02.meta.json +13 -0
  4. b2_8192_16384/part_03.meta.json +13 -0
  5. b2_8192_16384/part_04.meta.json +13 -0
  6. b2_8192_16384/part_05.meta.json +13 -0
  7. b2_8192_16384/part_08.meta.json +13 -0
  8. b2_8192_16384/part_11.meta.json +13 -0
  9. b2_8192_16384/part_12.meta.json +13 -0
  10. b2_8192_16384/part_18.meta.json +13 -0
  11. b2_8192_16384/part_19.meta.json +13 -0
  12. b2_8192_16384/part_20.meta.json +13 -0
  13. b2_8192_16384/part_21.meta.json +13 -0
  14. b2_8192_16384/part_22.meta.json +13 -0
  15. b2_8192_16384/part_24.meta.json +13 -0
  16. b2_8192_16384/part_25.meta.json +13 -0
  17. b2_8192_16384/part_26.meta.json +13 -0
  18. b2_8192_16384/part_27.meta.json +13 -0
  19. b2_8192_16384/part_31.meta.json +13 -0
  20. b2_8192_16384/part_33.meta.json +13 -0
  21. b2_8192_16384/part_36.meta.json +13 -0
  22. b2_8192_16384/part_38.meta.json +13 -0
  23. b2_8192_16384/part_41.meta.json +13 -0
  24. b2_8192_16384/part_43.meta.json +13 -0
  25. b2_8192_16384/part_45.meta.json +13 -0
  26. b2_8192_16384/part_46.meta.json +13 -0
  27. b2_8192_16384/part_47.meta.json +13 -0
  28. b2_8192_16384/part_50.meta.json +13 -0
  29. b2_8192_16384/part_51.meta.json +13 -0
  30. b2_8192_16384/part_62.meta.json +13 -0
  31. b2_8192_16384/part_63.meta.json +13 -0
  32. b2_8192_16384/part_64.meta.json +13 -0
  33. b2_8192_16384/part_65.meta.json +13 -0
  34. b2_8192_16384/part_67.meta.json +13 -0
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  37. b2_8192_16384/part_74.meta.json +13 -0
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  47. b2_8192_16384/part_91.meta.json +13 -0
  48. b2_8192_16384/part_92.meta.json +13 -0
  49. b2_8192_16384/part_94.meta.json +13 -0
  50. b2_8192_16384/part_95.meta.json +13 -0
README.md ADDED
@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: other
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+ language:
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+ - en
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+ task_categories:
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+ - text-generation
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+ tags:
8
+ - gpt2
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+ - tokenized
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+ - long-context
11
+ - pretraining
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+ size_categories:
13
+ - 10B<n<100B
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+ ---
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+
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+ # BluScriptCP tokenized token buckets (b1, b2, b3)
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+
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+ Pre-tokenized GPT-2 data for long-context training in the BluScriptCP / Context
19
+ Parallelism project. These are the three mid-length buckets of a token-bucketed,
20
+ deduplicated CommonCrawl-derived corpus (`new_full_2025_51+47`, dclm+betr).
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+
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+ Documents are bucketed by GPT-2 token length into half-open ranges `[lo, hi)`.
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+ Only b1, b2 and b3 are published here.
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+
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+ | bucket | token range | shards | documents | GPT-2 tokens | size |
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+ |---|---|---|---|---|---|
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+ | `b1_4096_8192` | [4,096, 8,192) | 96 | 2,571,614 | 13,977,775,051 | 28.0 GB |
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+ | `b2_8192_16384` | [8,192, 16,384) | 96 | 634,512 | 6,990,738,341 | 14.0 GB |
29
+ | `b3_16384_32768` | [16,384, 32,768) | 96 | 142,657 | 3,027,863,374 | 6.1 GB |
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+ | **total** | | **288** | **3,348,783** | **23,996,376,766** | **48.1 GB** |
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+
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+ ## Format
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+
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+ Tokenizer is `tiktoken` encoding `gpt2` (vocab 50,257), applied with
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+ `encode_ordinary`, which treats `<|endoftext|>`-like text as ordinary tokens.
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+
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+ Each of the 288 shards has three files under `<bucket>/`:
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+
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+ | file | dtype | contents |
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+ |---|---|---|
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+ | `part_XX.bin` | `uint16` | GPT-2 token ids, documents concatenated, no separator |
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+ | `part_XX.idx.npy` | `int64` | cumulative offsets, length `ndocs + 1` |
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+ | `part_XX.meta.json` | - | per-shard document count, token count, verification counters |
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+
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+ Token ids fit `uint16` losslessly since the GPT-2 vocabulary is 50,257 < 65,536.
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+
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+ **No EOT separator is inserted.** Document boundaries are carried exactly by the
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+ index, so a packing step must insert its own delimiter if one is needed.
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+
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+ ## Usage
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+
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+ ```python
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+ import numpy as np
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+
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+ stem = "b1_4096_8192/part_00"
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+ idx = np.load(stem + ".idx.npy") # int64, ndocs+1
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+ tok = np.memmap(stem + ".bin", dtype=np.uint16, mode="r") # uint16 stream
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+
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+ n_docs = len(idx) - 1
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+ doc_0 = tok[idx[0]:idx[1]] # document 0 as a uint16 array of token ids
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+
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+ import tiktoken
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+ enc = tiktoken.get_encoding("gpt2")
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+ print(enc.decode(doc_0.tolist())[:200])
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+ ```
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+
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+ To iterate every document in a shard:
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+
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+ ```python
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+ for i in range(len(idx) - 1):
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+ doc = tok[idx[i]:idx[i + 1]]
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+ ```
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+
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+ ## Verification
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+
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+ Every document in the source corpus carries a pre-computed `gpt2_tokens` count.
77
+ Each document's re-tokenized length was compared against it:
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+
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+ - **0 count mismatches across all 3,348,783 documents** in these three buckets
80
+ (and 0 across all 74,643,792 documents of the full 7-bucket corpus).
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+ - Document and token totals are an exact match to the corpus `summary.tsv`.
82
+ - Index integrity checked per shard: `len(bin) == idx[-1]`, `idx[0] == 0`, `idx`
83
+ strictly increasing, `max(token_id) < 50257`.
84
+ - Exact text roundtrip: `enc.decode(bin[idx[i]:idx[i+1]])` is byte-identical to the
85
+ source `content` field on sampled documents from every bucket.
86
+
87
+ ## Not included
88
+
89
+ The other four buckets of the corpus exist but are not published here:
90
+ `b0_lt_4096` (71.25 M docs, 73.59 B tokens), `b4_32768_65536`, `b5_65536_131072`,
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+ `b6_gte_131072`.
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+
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+ ## Provenance
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+
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+ Derived from CommonCrawl via the DCLM and BETR filtering pipelines, then
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+ deduplicated. Records retain their original scores and metadata upstream; only the
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+ token ids are published here. Respect the licensing and terms of the underlying
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+ CommonCrawl data.
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