| ---
|
| license: other
|
| language:
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| - km
|
| - en
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| multilinguality: multilingual
|
| size_categories:
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| - 1M<n<10M
|
| task_categories:
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| - text-generation
|
| - fill-mask
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| task_ids:
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| - language-modeling
|
| - masked-language-modeling
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| tags:
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| - khmer
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| - cambodia
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| - code-switching
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| - sentencepiece
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| - diffusion-lm
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| - low-resource
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| pretty_name: Khmer + English Mixed Text Corpus
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| configs:
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| - config_name: raw
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| default: true
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| data_files:
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| - split: train
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| path: all_text.txt
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| - config_name: segmented
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| data_files:
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| - split: train
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| 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`](https://huggingface.co/Panhapich/khmer-sp-8k) SentencePiece tokenizer and the
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| 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](#dataset-structure).
|
| - Khmer/English mixing is **entirely organic**: it comes from the real sources below (receipt
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| 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`](https://huggingface.co/Panhapich/khmer-sp-8k)). Ready for SentencePiece-style subword training without re-running segmentation yourself. |
|
|
|
| ```python
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| 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")
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| ```
|
|
|
| ### 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`](https://huggingface.co/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.
|
|
|
