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Publish Custom GPT 40M Sangraha training snapshot

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Apache 2.0 with sraivante copyright for original contributions; Sangraha raw corpus and derived tokens retain CC BY 4.0 attribution. Includes 699,050 documents, 411,919,454 train and 45,768,829 validation tokens, hashes, usage and measured conversion fidelity.

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ATTRIBUTION.md ADDED
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+ # Sangraha attribution and changes
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+
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+ Corpus source: AI4Bharat, Sangraha (IndicLLMSuite).
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+ Source URL: https://huggingface.co/datasets/ai4bharat/sangraha
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+ Pinned revision: 8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8
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+ Included source files: verified/eng/data-0.parquet and verified/eng/data-1.parquet.
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+
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+ The source repository declares Creative Commons Attribution 4.0 International:
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+ https://creativecommons.org/licenses/by/4.0/
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+ The full license is preserved in LICENSE. Original content creators retain their rights.
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+ This release makes no claim that sraivante wrote the third-party source texts.
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+
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+ Changes by sraivante / this release:
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+ - Selected two existing English shards for the project; raw bytes are unchanged.
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+ - Encoded the corpus with the project's custom 16,000-token byte-level BPE tokenizer,
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+ appended EOS after documents and split the token stream at 90% of total tokens.
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+ - Packaged the saved arrays, source identity, integrity hashes and original release documentation.
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+ No additional filtering, rewriting, redaction or deduplication was applied during publication.
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+ The tokenizer was fitted before the split; one document straddles the split.
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+
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+ Please retain this attribution and cite the source authors' work:
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+
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+ ```bibtex
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+ @article{khan2024indicllmsuite,
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+ title = {IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages},
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+ author = {Mohammed Safi Ur Rahman Khan and Priyam Mehta and Ananth Sankar and Umashankar Kumaravelan and Sumanth Doddapaneni and Suriyaprasaad G and Varun Balan G and Sparsh Jain and Anoop Kunchukuttan and Pratyush Kumar and Raj Dabre and Mitesh M. Khapra},
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+ year = {2024},
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+ journal = {arXiv preprint arXiv: 2403.06350}
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+ }
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+ ```
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+
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+ Copyright (c) 2026 sraivante, for original code, release documentation and original
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+ packaging contributions only, where applicable, under Apache License 2.0.
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+ See LICENSE-APACHE-2.0. This grant does not replace the corpus's CC BY 4.0 terms.
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NOTICE ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ AI4Bharat Sangraha English subset and derived token streams: CC BY 4.0.
2
+ See ATTRIBUTION.md, LICENSE and provenance/upstream_README.md.
3
+ Copyright (c) 2026 sraivante applies only to original project code, release
4
+ documentation and original packaging contributions where applicable, licensed
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+ under Apache License 2.0 (LICENSE-APACHE-2.0). Source texts are third-party works.
README.md ADDED
@@ -0,0 +1,142 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ pretty_name: Custom GPT 40M Sangraha Subset
3
+ license: cc-by-4.0
4
+ language:
5
+ - en
6
+ task_categories:
7
+ - text-generation
8
+ tags:
9
+ - language-modeling
10
+ - sangraha
11
+ - tokenized
12
+ - provenance
13
+ size_categories:
14
+ - 100K<n<1M
15
+ configs:
16
+ - config_name: corpus
17
+ default: true
18
+ data_files:
19
+ - split: corpus
20
+ path: raw/*.parquet
21
+ dataset_info:
22
+ config_name: corpus
23
+ features:
24
+ - name: doc_id
25
+ dtype: string
26
+ - name: text
27
+ dtype: string
28
+ - name: type
29
+ dtype: string
30
+ splits:
31
+ - name: corpus
32
+ num_examples: 699050
33
+ ---
34
+
35
+ # Custom GPT 40M — Sangraha training snapshot
36
+
37
+ This repository preserves the raw English corpus and saved token streams associated with **[sraivante/Custom-GPT-40M-Base](https://huggingface.co/sraivante/Custom-GPT-40M-Base)**, a 39.85M-parameter model trained from scratch in the CUSTOM_LLM3 / CUSTOM_LLM_OLLAMA_IMPORT project.
38
+
39
+ The corpus is an unchanged two-shard subset of **AI4Bharat Sangraha**, not original writing by sraivante. Original article authors, publishers, speakers and other contributors retain their rights. The corpus and its tokenized representation retain **CC BY 4.0** terms and upstream attribution.
40
+
41
+ ## Contents
42
+
43
+ | Artifact | Records / tokens | Storage |
44
+ |---|---:|---|
45
+ | `raw/data-0.parquet` | 349,525 documents | Original Parquet |
46
+ | `raw/data-1.parquet` | 349,525 documents | Original Parquet |
47
+ | `tokens/train_ids.npy` | 411,919,454 tokens | One-dimensional uint16 NumPy array |
48
+ | `tokens/val_ids.npy` | 45,768,829 tokens | One-dimensional uint16 NumPy array |
49
+ | `bpe_tokenizer.json` | 16,000 vocabulary entries | Original tokenizer |
50
+ | `provenance/data_audit.json` | Hashes, source identity, split details and verification | JSON |
51
+
52
+ The raw corpus contains **699,050 documents**: 689,417 tagged `web`, 9,578 `pdf` and 55 `speech`. Its exact original fields are:
53
+
54
+ | Field | Type | Meaning |
55
+ |---|---|---|
56
+ | `doc_id` | string | Original upstream document identifier |
57
+ | `text` | string | Extracted text used for tokenization |
58
+ | `type` | string | Upstream content category |
59
+
60
+ No null values were found in these three columns. The combined encoded corpus contains **457,688,283 tokens**, including 699,050 document-ending EOS markers.
61
+
62
+ ## Source and attribution
63
+
64
+ - Dataset: [AI4Bharat Sangraha](https://huggingface.co/datasets/ai4bharat/sangraha)
65
+ - Pinned revision: `8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8`
66
+ - Source files: [verified/eng/data-0.parquet](https://huggingface.co/datasets/ai4bharat/sangraha/blob/8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8/verified/eng/data-0.parquet) and [verified/eng/data-1.parquet](https://huggingface.co/datasets/ai4bharat/sangraha/blob/8b813c3f62d37b2fa174d68c31e8b35ae2fe85e8/verified/eng/data-1.parquet)
67
+ - Upstream license: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)
68
+ - Research paper: [IndicLLMSuite](https://arxiv.org/abs/2403.06350)
69
+ - Preserved upstream description and citation: [provenance/upstream_README.md](provenance/upstream_README.md)
70
+ - Credit and modification statement: [ATTRIBUTION.md](ATTRIBUTION.md)
71
+
72
+ Both raw files are byte-for-byte identical to that upstream revision, verified using the upstream LFS SHA-256 identifiers. Raw data was not rewritten, shuffled or filtered for this release.
73
+
74
+ The tokenized files are the project's saved arrays. They encode the original text in shard order with one `<eos>` token after each document. This tokenization and the train/validation split are transformations of the upstream corpus.
75
+
76
+ ## Splitting and historical identity
77
+
78
+ The notebook concatenates `data-0.parquet` then `data-1.parquet`, encodes texts using its custom BPE tokenizer, appends EOS, and cuts at `floor(0.9 * total_tokens)`. The training array is the prefix and validation is the suffix.
79
+
80
+ **The split is not document-disjoint.** Document index 629,027, counting from zero, contributes 1,620 tokens to training and its remaining 294 tokens to validation. The tokenizer is fitted before this split in the notebook and can therefore learn vocabulary from validation text.
81
+
82
+ Raw documents are exposed as a single `corpus` split. This avoids assigning entire documents to token-level train/validation splits they do not exactly match. Use the saved arrays for the exact training/validation boundaries.
83
+
84
+ The packaged raw hashes match the upstream snapshot. All 261 deterministic sampled documents, including shard endpoints and the split-boundary document, reproduced their saved token spans exactly. This was a sample-based token reconstruction check, not a complete re-tokenization. The arrays have complete SHA-256 hashes and are copied unchanged from the local training artifacts. Their counts, matching samples, notebook and checkpoint metadata support the association with the run; the historical run has no external file-access log.
85
+
86
+ There is no separate test split or instruction fine-tuning dataset for this model. The reported validation loss uses random token windows from `val_ids.npy`.
87
+
88
+ ## Load the corpus
89
+
90
+ ```python
91
+ from datasets import load_dataset
92
+
93
+ corpus = load_dataset(
94
+ "sraivante/Custom-GPT-40M-Sangraha-Subset",
95
+ "corpus",
96
+ split="corpus",
97
+ streaming=True,
98
+ )
99
+ row = next(iter(corpus))
100
+ print(row.keys()) # doc_id, text, type
101
+ ```
102
+
103
+ ## Load the exact token streams
104
+
105
+ ```python
106
+ import numpy as np
107
+ from huggingface_hub import hf_hub_download
108
+ from tokenizers import Tokenizer
109
+
110
+ repo = "sraivante/Custom-GPT-40M-Sangraha-Subset"
111
+ train_path = hf_hub_download(repo, "tokens/train_ids.npy", repo_type="dataset")
112
+ val_path = hf_hub_download(repo, "tokens/val_ids.npy", repo_type="dataset")
113
+ tok_path = hf_hub_download(repo, "bpe_tokenizer.json", repo_type="dataset")
114
+
115
+ train = np.load(train_path, mmap_mode="r", allow_pickle=False)
116
+ val = np.load(val_path, mmap_mode="r", allow_pickle=False)
117
+ tokenizer = Tokenizer.from_file(tok_path)
118
+
119
+ assert train.shape == (411919454,)
120
+ assert val.shape == (45768829,)
121
+ # Next-token training window; promote uint16 IDs to int64 for PyTorch.
122
+ x = train[:256].astype("int64")
123
+ y = train[1:257].astype("int64")
124
+ ```
125
+
126
+ Special tokens: `<unk>=0`, `<pad>=1`, `<bos>=2`, `<eos>=3`. The tokenizer uses byte-level BPE with `add_prefix_space=True`; it does not automatically insert BOS or EOS. The corpus tokenizer was trained on text with line breaks normalized for tokenizer training, while the saved token arrays encode original text. Not every possible character is necessarily covered by its learned vocabulary.
127
+
128
+ The raw files and arrays require approximately 2.11 GB together, before any Arrow cache or model files. Pin a repository commit in `revision=...` for later reproducibility; file hashes are also included in `SHA256SUMS`.
129
+
130
+ ## Appropriate uses and limitations
131
+
132
+ This snapshot supports reproducibility, English causal language modeling and small-model experiments. It represents only two English shards of Sangraha. It is not a representative sample of all English or all of Sangraha, and it contains no instruction/response annotations.
133
+
134
+ Web articles, PDF extractions and transcriptions may contain mistakes, duplicated text, extraction noise, biases, offensive material, and names or other personal information that was present in the public sources. No additional de-identification, redaction, deduplication or content moderation was performed for this publication. The data should not be treated as verified ground truth or an independent downstream benchmark.
135
+
136
+ ## Copyright and licensing
137
+
138
+ The **corpus and token arrays are CC BY 4.0**; preserve AI4Bharat / Sangraha attribution, acknowledge original content creators, link the license and state your modifications. See [LICENSE](LICENSE) and [ATTRIBUTION.md](ATTRIBUTION.md).
139
+
140
+ **Copyright (c) 2026 sraivante** applies only to original project code, original release documentation and original packaging contributions where copyright exists. Those contributions are licensed under [Apache License 2.0](LICENSE-APACHE-2.0). This does not transfer ownership of, or relicense, the third-party text. The model weights have their own Apache-2.0 release.
141
+
142
+ Snapshot version: **2026-09-25**. Model, dataset, tokenizer, source revision and hashes are linked for this authorized release.
SHA256SUMS ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ b8318ac40ad81eb21cc8f20261fc3242cba76119cac05cf5f717b7c6f16d497d .gitattributes
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+ 9fe9c519e898fa33a38f294604ff1a8057d7f5f2b853191893d0d81e5568911a artifact_integrity.json
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+ 54db7ec78aa51785651c913cdac0580ede00d574c2bf48727e95b9099824710e ATTRIBUTION.md
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+ 178a7ba7499dc94c3dbe3c52f1ba5e5db3c737018b9b361211ea8b6f28a7f60f bpe_tokenizer.json
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+ 9ba9550ad48438d0836ddab3da480b3b69ffa0aac7b7878b5a0039e7ab429411 LICENSE
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+ 3ddf9be5c28fe27dad143a5dc76eea25222ad1dd68934a047064e56ed2fa40c5 LICENSE-APACHE-2.0
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+ 88663b3c4f85e0f0abb019f0cf456c30f7069ca990f56d6731441bc240b02816 NOTICE
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+ 59eb225e402099af329eb8fcbdea3316061b4d60af45dad0f98c68a31e0df103 provenance/data_audit.json
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+ 93803f5c71a78da1a22ddf1753f0e4114cb837b3ac8e789b4ec2a88a12ddb637 provenance/upstream_README.md
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+ 3c080ef54239f17351162353e9ee957f9fdd6d158570e27186480805231bdade raw/data-0.parquet
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+ 5fadacb535e8eeb17326cf909e86e54aa49d8ad189da73e5801a08e358f5375e raw/data-1.parquet
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+ dbff7dd854e5ec60d86902c83d895bfe76c699dcf83580126f85dc3a8900cf38 README.md
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+ 11268ecb2281adf1cdc6340df8f9517931afa2af166aa5099a114edb69e6210b tokens/train_ids.npy
14
+ c9f6e7c13916ce8987a9caa80bfef4957e648ade97f30ffbb9ebacd943e736ee tokens/val_ids.npy
artifact_integrity.json ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "version": "2026-09-25",
3
+ "covers": "All payload files except artifact_integrity.json and SHA256SUMS",
4
+ "files": {
5
+ ".gitattributes": {
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+ "bytes": 141,
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+ "sha256": "b8318ac40ad81eb21cc8f20261fc3242cba76119cac05cf5f717b7c6f16d497d"
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+ },
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+ "bytes": 1937,
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+ "sha256": "54db7ec78aa51785651c913cdac0580ede00d574c2bf48727e95b9099824710e"
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+ },
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+ "tokens": 208
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+ },
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+ {
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+ "document_index_zero_based": 699049,
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+ "doc_id": "5a033a9187d685d7da0550bd538011b63a3082cd",
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+ "tokens": 638
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+ }
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+ ]
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+ },
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+ "raw_null_counts": {
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+ "doc_id": 0,
1392
+ "text": 0,
1393
+ "type": 0
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+ },
1395
+ "raw_type_counts": {
1396
+ "web": 689417,
1397
+ "pdf": 9578,
1398
+ "speech": 55
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+ },
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+ "training_notebook_sha256": "6dcddbcbec22b59c15ba360021f3e9495539383fb24448a946c2d1dd660d1aca",
1401
+ "both_source_notebooks_identical": true,
1402
+ "notebook_saved_outputs": 0,
1403
+ "copyright_scope": "sraivante: original model, training code and release documentation only; Sangraha and underlying texts retain upstream attribution and CC BY 4.0 terms."
1404
+ }
provenance/upstream_README.md ADDED
@@ -0,0 +1,348 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: cc-by-4.0
3
+ task_categories:
4
+ - text-generation
5
+ language:
6
+ - as
7
+ - bn
8
+ - gu
9
+ - en
10
+ - hi
11
+ - kn
12
+ - ks
13
+ - ml
14
+ - mr
15
+ - ne
16
+ - or
17
+ - pa
18
+ - sa
19
+ - sd
20
+ - ta
21
+ - te
22
+ - ur
23
+ tags:
24
+ - language-modeling
25
+ - casual-lm
26
+ - llm
27
+ pretty_name: sangraha
28
+ dataset_info:
29
+ - config_name: verified
30
+ features:
31
+ - name: doc_id
32
+ dtype: string
33
+ - name: type
34
+ dtype: string
35
+ - name: text
36
+ dtype: string
37
+ splits:
38
+ - name: asm
39
+ - name: ben
40
+ - name: brx
41
+ - name: doi
42
+ - name: eng
43
+ - name: gom
44
+ - name: guj
45
+ - name: hin
46
+ - name: kan
47
+ - name: kas
48
+ - name: mai
49
+ - name: mal
50
+ - name: mar
51
+ - name: mni
52
+ - name: nep
53
+ - name: ori
54
+ - name: pan
55
+ - name: san
56
+ - name: sat
57
+ - name: snd
58
+ - name: tam
59
+ - name: tel
60
+ - name: urd
61
+ - config_name: unverified
62
+ features:
63
+ - name: doc_id
64
+ dtype: string
65
+ - name: text
66
+ dtype: string
67
+ splits:
68
+ - name: asm
69
+ - name: ben
70
+ - name: guj
71
+ - name: hin
72
+ - name: kan
73
+ - name: mal
74
+ - name: mar
75
+ - name: nep
76
+ - name: ori
77
+ - name: pan
78
+ - name: san
79
+ - name: tam
80
+ - name: tel
81
+ - name: urd
82
+ - config_name: synthetic
83
+ features:
84
+ - name: doc_id
85
+ dtype: string
86
+ - name: text
87
+ dtype: string
88
+ splits:
89
+ - name: asm_Beng
90
+ - name: asm_Latn
91
+ - name: ben_Beng
92
+ - name: ben_Latn
93
+ - name: guj_Gujr
94
+ - name: guj_Latn
95
+ - name: hin_Deva
96
+ - name: hin_Latn
97
+ - name: kan_Knda
98
+ - name: kan_Latn
99
+ - name: mal_Mlym
100
+ - name: mal_Latn
101
+ - name: mar_Deva
102
+ - name: mar_Latn
103
+ - name: npi_Deva
104
+ - name: npi_Latn
105
+ - name: ory_Orya
106
+ - name: ory_Latn
107
+ - name: pan_Guru
108
+ - name: pan_Latn
109
+ - name: san_Deva
110
+ - name: san_Latn
111
+ - name: tam_Taml
112
+ - name: tam_Latn
113
+ - name: tel_Telu
114
+ - name: tel_Latn
115
+ - name: urd_Arab
116
+ - name: urd_Latn
117
+
118
+ configs:
119
+ - config_name: verified
120
+ data_files:
121
+ - split: asm
122
+ path: verified/asm/*.parquet
123
+ - split: ben
124
+ path: verified/ben/*.parquet
125
+ - split: brx
126
+ path: verified/brx/*.parquet
127
+ - split: doi
128
+ path: verified/doi/*.parquet
129
+ - split: eng
130
+ path: verified/eng/*.parquet
131
+ - split: gom
132
+ path: verified/gom/*.parquet
133
+ - split: guj
134
+ path: verified/guj/*.parquet
135
+ - split: hin
136
+ path: verified/hin/*.parquet
137
+ - split: kan
138
+ path: verified/kan/*.parquet
139
+ - split: kas
140
+ path: verified/kas/*.parquet
141
+ - split: mai
142
+ path: verified/mai/*.parquet
143
+ - split: mal
144
+ path: verified/mal/*.parquet
145
+ - split: mar
146
+ path: verified/mar/*.parquet
147
+ - split: mni
148
+ path: verified/mni/*.parquet
149
+ - split: nep
150
+ path: verified/nep/*.parquet
151
+ - split: ori
152
+ path: verified/ori/*.parquet
153
+ - split: pan
154
+ path: verified/pan/*.parquet
155
+ - split: san
156
+ path: verified/san/*.parquet
157
+ - split: sat
158
+ path: verified/sat/*.parquet
159
+ - split: snd
160
+ path: verified/snd/*.parquet
161
+ - split: tam
162
+ path: verified/tam/*.parquet
163
+ - split: tel
164
+ path: verified/tel/*.parquet
165
+ - split: urd
166
+ path: verified/urd/*.parquet
167
+ - config_name: unverified
168
+ data_files:
169
+ - split: asm
170
+ path: unverified/asm/*.parquet
171
+ - split: ben
172
+ path: unverified/ben/*.parquet
173
+ - split: guj
174
+ path: unverified/guj/*.parquet
175
+ - split: hin
176
+ path: unverified/hin/*.parquet
177
+ - split: kan
178
+ path: unverified/kan/*.parquet
179
+ - split: mal
180
+ path: unverified/mal/*.parquet
181
+ - split: mar
182
+ path: unverified/mar/*.parquet
183
+ - split: nep
184
+ path: unverified/nep/*.parquet
185
+ - split: ori
186
+ path: unverified/ori/*.parquet
187
+ - split: pan
188
+ path: unverified/pan/*.parquet
189
+ - split: san
190
+ path: unverified/san/*.parquet
191
+ - split: tam
192
+ path: unverified/tam/*.parquet
193
+ - split: tel
194
+ path: unverified/tel/*.parquet
195
+ - split: urd
196
+ path: unverified/urd/*.parquet
197
+ - config_name: synthetic
198
+ data_files:
199
+ - split: asm_Beng
200
+ path: synthetic/asm_Beng/*.parquet
201
+ - split: asm_Latn
202
+ path: synthetic/asm_Latn/*.parquet
203
+ - split: ben_Beng
204
+ path: synthetic/ben_Beng/*.parquet
205
+ - split: ben_Latn
206
+ path: synthetic/ben_Latn/*.parquet
207
+ - split: guj_Gujr
208
+ path: synthetic/guj_Gujr/*.parquet
209
+ - split: guj_Latn
210
+ path: synthetic/guj_Latn/*.parquet
211
+ - split: hin_Deva
212
+ path: synthetic/hin_Deva/*.parquet
213
+ - split: hin_Latn
214
+ path: synthetic/hin_Latn/*.parquet
215
+ - split: kan_Knda
216
+ path: synthetic/kan_Knda/*.parquet
217
+ - split: kan_Latn
218
+ path: synthetic/kan_Latn/*.parquet
219
+ - split: mal_Mlym
220
+ path: synthetic/mal_Mlym/*.parquet
221
+ - split: mal_Latn
222
+ path: synthetic/mal_Latn/*.parquet
223
+ - split: mar_Deva
224
+ path: synthetic/mar_Deva/*.parquet
225
+ - split: mar_Latn
226
+ path: synthetic/mar_Latn/*.parquet
227
+ - split: npi_Deva
228
+ path: synthetic/npi_Deva/*.parquet
229
+ - split: npi_Latn
230
+ path: synthetic/npi_Latn/*.parquet
231
+ - split: ory_Orya
232
+ path: synthetic/ory_Orya/*.parquet
233
+ - split: ory_Latn
234
+ path: synthetic/ory_Latn/*.parquet
235
+ - split: pan_Guru
236
+ path: synthetic/pan_Guru/*.parquet
237
+ - split: pan_Latn
238
+ path: synthetic/pan_Latn/*.parquet
239
+ - split: san_Deva
240
+ path: synthetic/san_Deva/*.parquet
241
+ - split: san_Latn
242
+ path: synthetic/san_Latn/*.parquet
243
+ - split: tam_Taml
244
+ path: synthetic/tam_Taml/*.parquet
245
+ - split: tam_Latn
246
+ path: synthetic/tam_Latn/*.parquet
247
+ - split: tel_Telu
248
+ path: synthetic/tel_Telu/*.parquet
249
+ - split: tel_Latn
250
+ path: synthetic/tel_Latn/*.parquet
251
+ - split: urd_Arab
252
+ path: synthetic/urd_Arab/*.parquet
253
+ - split: urd_Latn
254
+ path: synthetic/urd_Latn/*.parquet
255
+
256
+ size_categories:
257
+ - 100B<n<1T
258
+ ---
259
+
260
+ # Sangraha
261
+
262
+ <p align="center">
263
+ <img src="https://cdn-uploads.huggingface.co/production/uploads/63ef3cd11e695b35aa48bebc/nDnyidcqIOLAP9dTw9GrK.png" />
264
+ </p>
265
+
266
+ Sangraha is the largest high-quality, cleaned Indic language pretraining data containing 251B tokens summed up over 22 languages, extracted from curated sources, existing multilingual corpora and large scale translations.
267
+
268
+
269
+ **More information**:
270
+
271
+ - For detailed information on the curation and cleaning process of Sangraha, please checkout our paper [on Arxiv](https://arxiv.org/abs/2403.06350);
272
+ - Check out the scraping and cleaning pipelines used to curate Sangraha [on GitHub](https://github.com/AI4Bharat/IndicLLMSuite);
273
+
274
+ ## Getting Started
275
+
276
+ For downloading the entire Sangraha:
277
+
278
+ ```python
279
+ from datasets import load_dataset
280
+
281
+ dataset = load_dataset("ai4bharat/sangraha")
282
+ ```
283
+
284
+ For downloading a subset (Verified/Unverified) of Sangraha:
285
+
286
+ ```python
287
+ from datasets import load_dataset
288
+
289
+ dataset = load_dataset("ai4bharat/sangraha", data_dir="<subset_name>")
290
+ # for example: dataset = load_dataset("ai4bharat/sangraha", data_dir="verified")
291
+ ```
292
+
293
+ For downloading one language from a subset of Sangraha:
294
+
295
+ ```python
296
+ from datasets import load_dataset
297
+
298
+ dataset = load_dataset("ai4bharat/sangraha", data_dir="<subset_name>/<lang_code>")
299
+ # for example: dataset = load_dataset("ai4bharat/sangraha", data_dir="verified/asm")
300
+ ```
301
+
302
+ ## Background
303
+
304
+ Sangraha contains three broad components:
305
+
306
+ - **Sangraha Verified**: Containing scraped data from "human-verified" Websites, OCR-extracted data from high quality Indic language PDFs, transcribed data from various Indic language videos, podcasts, movies, courses, etc.
307
+ - **Sangraha Unverfied**: High quality Indic language data extracted from existing multilingual corpora employing perplexity filtering using n-gram language models trained on Sangraha Verified.
308
+ - **Sangraha Synthetic**: WikiMedia English translated to 14 Indic languages and further "romanised" from 14 languages by transliteration to English.
309
+
310
+ ## Data Statistics
311
+
312
+ | **Lang Code** | **Verified** | **Synthetic** | **Unverified** | **Total Tokens (in Millions)** |
313
+ | ------------- | ------------ | ------------- | -------------- | ------------------------------ |
314
+ | asm | 292.1 | 11,696.4 | 17.5 | 12,006.0 |
315
+ | ben | 10,604.4 | 13,814.1 | 5,608.8 | 30,027.5 |
316
+ | brx | 1.5 | - | - | 1.5 |
317
+ | doi | 0.06 | - | - | 0.06 |
318
+ | eng | 12,759.9 | - | - | 12,759.9 |
319
+ | gom | 10.1 | - | - | 10.1 |
320
+ | guj | 3,647.9 | 12,934.5 | 597.0 | 17,179.4 |
321
+ | hin | 12,617.3 | 9,578.7 | 12,348.3 | 34,544.3 |
322
+ | kan | 1,778.3 | 12,087.4 | 388.8 | 14,254.5 |
323
+ | kas | 0.5 | - | - | 0.5 |
324
+ | mai | 14.6 | - | - | 14.6 |
325
+ | mal | 2,730.8 | 13,130.0 | 547.8 | 16,408.6 |
326
+ | mar | 2,827.0 | 10,816.7 | 652.1 | 14,295.8 |
327
+ | mni | 7.4 | - | - | 7.4 |
328
+ | npi | 1,822.5 | 10,588.7 | 485.5 | 12,896.7 |
329
+ | ori | 1,177.1 | 11,338.0 | 23.7 | 12,538.8 |
330
+ | pan | 1,075.3 | 9,969.6 | 136.9 | 11,181.8 |
331
+ | san | 1,329.0 | 13,553.5 | 9.8 | 14,892.3 |
332
+ | sat | 0.3 | - | - | 0.3 |
333
+ | snd | 258.2 | - | - | 258.2 |
334
+ | tam | 3,985.1 | 11,859.3 | 1,515.9 | 17,360.3 |
335
+ | urd | 3,658.1 | 9,415.8 | 1,328.2 | 14,402.1 |
336
+ | tel | 3,706.8 | 11,924.5 | 647.4 | 16,278.7 |
337
+ | **Total** | **64,306.1** | **162,707.9** | **24,307.7** | **251,321.0** |
338
+
339
+ To cite Sangraha, please use:
340
+
341
+ ```
342
+ @article{khan2024indicllmsuite,
343
+ title = {IndicLLMSuite: A Blueprint for Creating Pre-training and Fine-Tuning Datasets for Indian Languages},
344
+ author = {Mohammed Safi Ur Rahman Khan and Priyam Mehta and Ananth Sankar and Umashankar Kumaravelan and Sumanth Doddapaneni and Suriyaprasaad G and Varun Balan G and Sparsh Jain and Anoop Kunchukuttan and Pratyush Kumar and Raj Dabre and Mitesh M. Khapra},
345
+ year = {2024},
346
+ journal = {arXiv preprint arXiv: 2403.06350}
347
+ }
348
+ ```
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