Datasets:

Panhapich
/

khmer-text-corpus

license: other
language:
  - km
  - en
multilinguality: multilingual
size_categories:
  - 1M<n<10M
task_categories:
  - text-generation
  - fill-mask
task_ids:
  - language-modeling
  - masked-language-modeling
tags:
  - khmer
  - cambodia
  - code-switching
  - sentencepiece
  - diffusion-lm
  - low-resource
pretty_name: Khmer + English Mixed Text Corpus
configs:
  - config_name: raw
    default: true
    data_files:
      - split: train
        path: all_text.txt
  - config_name: segmented
    data_files:
      - split: train
        path: all_text_segmented.txt

Khmer + English Mixed Text Corpus

A cleaned, deduplicated Khmer text corpus with naturally-occurring Khmer/English code-switching (English tech & finance terms, Latin script, and digits embedded in Khmer text). It is the training data for the Panhapich/khmer-sp-8k SentencePiece tokenizer and the text-only warm-start of a shared Khmer diffusion decoder.

Dataset summary

4,893,739 sentences, one per line, UTF-8, deduplicated and shuffled (fixed seed 42, reproducible).
Two parallel versions of the same corpus are provided as separate configs — see Dataset structure.
Khmer/English mixing is entirely organic: it comes from the real sources below (receipt line items, tech/finance terms in headlines and raw text). No synthetic code-switching is injected anywhere in this corpus.

Dataset structure

Configs / files

Config	File	Description
`raw` (default)	`all_text.txt`	Natural Khmer text, no artificial word-boundary spaces. General-purpose use.
`segmented`	`all_text_segmented.txt`	Same 4,893,739 sentences, run through `khmer-nltk` word segmentation + gazetteer/Latin masking (see `Panhapich/khmer-sp-8k`). Ready for SentencePiece-style subword training without re-running segmentation yourself.

from datasets import load_dataset

raw = load_dataset("Panhapich/khmer-text-corpus", "raw")             # or omit the config name, it's the default
segmented = load_dataset("Panhapich/khmer-text-corpus", "segmented")

Data instances

Each row is a single field, text: one normalized sentence (or, in the segmented config, one word-segmented sentence with explicit spaces between Khmer words).

Data splits

A single train split — this is raw pretraining/tokenizer-training text, not a supervised task with held-out evaluation labels.

Dataset creation

Source data

Built from exactly four sources, mixed together, deduplicated, and shuffled. A global cap (5,000,000 sentences) bounds the total — small sources and the local file are taken in full, and the large raw-text source fills the remainder:

Source	Field	Description	Collected (pre-dedup)	Share
`nphearum/khmer-raw-text-3M-v2`	text	Large raw Khmer text (split on the Khmer full stop `។`)	4,836,355	96.7%
`Sokheng/khmer-synthetic-ocr-v1-100k`	text	Synthetic OCR receipt/label text (Khmer/English/digit mixed)	93,293	1.9%
`khmer_corpus.txt`	line	Pre-chunked ~1000-character Khmer text blocks (local file)	35,373	0.7%
`rinabuoy/aupp-assignment-data`	title	Khmer news headlines	34,991	0.7%

The Khmer/English mixing is entirely organic — it comes from the real sources above. No synthetic code-switching is injected.

Curation / preprocessing

Every sentence is normalized: coerce to string, strip, replace newlines/tabs with a single space, collapse repeated whitespace, and drop empty/invalid labels (???, NULL, N/A, UNKNOWN). Khmer word-spacing, punctuation, Khmer + Arabic numerals, and English/Latin tokens are preserved. Hub-source sentences are kept only if 5-400 characters; the local blocks are exempt from the length cap. The corpus is deduplicated and shuffled with a fixed seed (42) for reproducibility.

The segmented config additionally runs khmer-nltk word segmentation, with English/digit spans and known loanwords/acronyms (ATM, ABA, PDF, ...) masked out beforehand so the segmenter doesn't shatter them, and glued English/Latin runs (e.g. tryAImodel) further decomposed via frequency-based word segmentation, guarded by a domain exception list (ACLEDA, Bakong, COVID-19, 5G, ...) so real terms aren't mangled. Full pipeline: khmer_segmentation.py in Panhapich/khmer-sp-8k.

Considerations for using the data

Known noise: the local khmer_corpus.txt source and the raw-web-scraped Hub source contain some non-standard spelling, stray symbols, and OCR artifacts. This is real-world scraped text, not a curated, edited corpus.
Not filtered for PII beyond the basic invalid-label rules above.
Plain text only — no labels, translations, or task annotations.
Segmentation is a statistical CRF model, not a hand-built rule system — it generalizes well to ordinary Khmer but was not evaluated line-by-line against this specific corpus; treat the segmented config as "reduces cross-word token fusion," not "hand-verified perfect."

Licensing

This corpus is derived from third-party datasets and inherits their licenses. Confirm the terms of each source above and set the license field accordingly — other is a placeholder.