Neam (នាម): Khmer news NER

Neam finds people (PER), organizations (ORG) and places (LOC) in Khmer text and returns character offsets in the original input. It was trained and evaluated on Khmer news.

Use the neam package to run this model. It is an ensemble of three ALBERT checkpoints (members/), combined by ensemble.json with Viterbi decoding, overlapping context windows and Khmer-specific display rules. Loading one member with the standard transformers pipeline gives noticeably worse results.

pip install neam

from neam import Neam

ner = Neam.from_pretrained("MoriartyPuth/neam")
result = ner.predict("លោក ហ៊ុន ម៉ាណែត បានជួបជាមួយក្រសួងសុខាភិបាលនៅរាជធានីភ្នំពេញ។")
for entity in result["entities"]:
    print(entity["label"], entity["text"], entity["start"], entity["end"])

Model details

• Architecture: three fine-tuned seanghay/albert-khmer-small token classifiers (seeds 44, 42, 43), with softmax probabilities averaged. Labels are IOB2 over khmer-nltk words, read from each word's first non-▁ subword.
• Decoding: constrained IOB2 Viterbi with calibrated label scores (O × 0.9, ORG × 1.2, LOC × 0.85). Each word is read in the overlapping 128-subword window that gives it the most context.
• Display rules:
  • A short honorific such as លោក is joined to a following name.
  • Common place designators (ខេត្ត, ស្រុក, …) are completed.
  • A LOC directly after ជនជាតិ (a nationality phrase) is removed.
  • Sentence punctuation is trimmed.
• Size: about 37 MB per member, 113 MB in total. It runs on a CPU.

Intended use and annotation policy

Neam is for tagging named entities in Khmer news text, for search, indexing or reading aids. It follows docs/annotation_policy.md, in brief:

• PER covers names. Ceremonial titles (ឯកឧត្តម, សម្តេច, លោកជំទាវ) and ranks are excluded; short honorifics (លោក, លោកស្រី) are included.
• ORG covers named institutions, including their designator and any country or province in the name (for example រាជរដ្ឋាភិបាលកម្ពុជា and អយ្យការអមសាលាដំបូងខេត្តព្រះសីហនុ).
• LOC covers places and facilities.
• Nationality phrases (ជនជាតិ + country), products, events and storms are not entities.

Training data

• Cleaned Khmer WikiANN and a 1,200-record Khmer fiNERweb sample (PER/ORG/LOC labels only).
• 56 Khmer news articles from Fresh News, Khmer Press, Kampuchea Thmey and Cambodia Daily, plus 82 short replay examples, labelled at character level under the policy. The labels are assistant drafts, following the project owner's policy decisions. A sample of five articles (121 labels) was reviewed by the owner, a Khmer reader, with no errors found.
• A small synthetic curriculum contrasting nationality phrases with the same country used as a place.

Publisher article text is not redistributed with this model.

Training settings: 5 epochs, learning rate 3e-5, batch size 8 with 2-step gradient accumulation, frozen embeddings, and the news data weighted ×3.

Evaluation

Exact character-span F1: an entity counts only if both its boundaries and its type match.

Set	Articles / entities	F1	Precision	Recall	PER	ORG	LOC
Validation	5 / 108	78.03	75.7	80.6	80.0	73.8	83.5
Held-out (never used for training)	10 / 199	84.21	84.0	84.4	88.2	68.7	93.4

The PER, ORG and LOC columns are F1. For comparison, the project's earliest model (WikiANN-centred, no news-specific methods) scores 59.5 F1 on the same held-out set.

Seed variance. Retraining the same recipe with seven different seeds gives single-model held-out F1 between 78.0 and 85.4. Three-seed ensembles average 82.0 (range 79.6–85.8). This release's ensemble is on the favourable side of that range. Expect about 82–83 F1 on similar Khmer news.

Limitations

• Domain. Evaluated only on news from three Cambodian publishers, collected in September–October 2026. Accuracy on social media, government documents, informal or older text is untested and likely lower.
• Organization boundaries are the main weakness (about 69 ORG F1 on held-out). Long institutional names are sometimes cut short or split into designator plus name.
• Small evaluation sets. 307 entities in total, labelled by the same process as the training data. There has been no independent second-annotator agreement study.
• Segmentation. It relies on khmer-nltk word segmentation. Unusual spellings and transliterated foreign names can be segmented in ways that hurt boundaries.
• Scores in the output are model confidences, not calibrated probabilities of correctness.

License and attribution

• This model and the neam code are released under Apache-2.0.
• Base model: seanghay/albert-khmer-small (Apache-2.0).
• Training data:
  • whoisjones/fiNERweb (MIT).
  • unimelb-nlp/wikiann. Its license is listed as unknown on the Hub; it is derived from Wikipedia.
  • News articles remain the property of their publishers and are not redistributed.