Use this model to identify Korean time expressions in sentences or utterances. It predicts token-level BIO labels and can be used through the Hugging Face token-classification pipeline.

This is an experimental model trained for TIMEX3 span detection. It does not extract EVENT or TLINK annotations.

Training Data

The model was trained on the official Training split and evaluated on the official Validation split of 158.시간 표현 탐지 데이터.

Training/evaluation preprocessing:

Unsupported, empty, malformed, or unalignable TIMEX3 spans are excluded.
Records whose TIMEX3 span would be truncated by max_length=256 are excluded.
TIMEX-free records are retained as negative examples.
JSON text fields are used as the source text.

Training Configuration

bash

python -m time_expression_classifier.train_token_classifier \
  --data-root "158.시간 표현 탐지 데이터" \
  --model-name jhgan/ko-sroberta-multitask \
  --output-dir outputs/official_epoch2 \
  --split-mode official \
  --epochs 2 \
  --learning-rate 3e-5 \
  --batch-size 16 \
  --max-length 256

Key settings:

setting	value
backbone	`jhgan/ko-sroberta-multitask`
epochs	2
learning rate	3e-5
batch size	16
max length	256
weight decay	0.01
warmup ratio	0.06
seed	42

Evaluation

Metrics are entity-level exact match on the official Validation split.

metric	value
entity precision	0.8265
entity recall	0.8268
entity F1	0.8266
token accuracy	0.9899
eval loss	0.0350

Per-label entity-level results:

label	precision	recall	F1	support
DATE	0.8495	0.8367	0.8430	23422
TIME	0.7933	0.8033	0.7983	3665
DURATION	0.7848	0.8247	0.8042	6810
SET	0.7107	0.6910	0.7007	974

Usage

python

from transformers import pipeline

tagger = pipeline(
    "token-classification",
    model="kwoncho/ko-sroberta-korean-time-expression-classifier",
    aggregation_strategy="simple",
)

text = "매주 토요일 저녁에 회의를 합니다."
print(tagger(text))

Limitations

The model is sensitive to ambiguous time expressions such as 주, 하루, 시간, 한달, 일주일, and 매일.
SET is the lowest-performing label due to smaller support and ambiguity between repeated events and duration expressions.
The model predicts TIMEX3 spans only. Normalization to calendar values is not included.
Evaluation uses exact span match, so partial boundary differences count as errors.

Reproducibility

Repository: git@github.com:hyun2019/ko-sroberta-korean-time-expression-classifier.git

The local release artifact is tracked as models/official_epoch2 via DVC.