KF-DeBERTa-base for KLUE Named Entity Recognition
This model fine-tunes kakaobank/kf-deberta-base for Korean named entity recognition. The head predicts 13 BIO labels: six entity types plus O. The pretrained encoder revision used for training was 363b171d71443b0874b0bf9cea053eb5b1650633.
Results
| Data | Metric | F1 |
|---|---|---|
| KLUE NER public validation, 5,000 examples | Entity macro F1, strict IOB2, character reconstructed | 86.74% |
| Same validation, before the final lower-rate epoch | Same metric | 86.50% |
The score was obtained after reloading the saved checkpoint and evaluating in FP32. The scorer follows the KLUE baseline protocol: character predictions are reconstructed, ordinary ASCII spaces are removed, all examples are flattened, and seqeval computes strict IOB2 entity macro F1. The implementation scores the active SEP position; the original fixed-length baseline may score PAD at its last position, so this is baseline-compatible rather than bit-for-bit identical.
The reported 86.11% KoELECTRA baseline from the KLUE paper uses a hidden test split. This 86.74% is from public validation and does not establish superiority on that hidden test. Model selection, the extra epoch, and a weight-average comparison used the same validation split; only one training seed was run.
Training
- Dataset: KLUE NER, 21,008 training examples.
- Base model:
kakaobank/kf-deberta-basewith a randomly initialized token-classification head. - Initial training: 5 epochs, seed 42, batch size 16, learning rate
3e-5, AdamW weight decay 0.01, warmup 10%, max length 128, BF16 autocast. - Continuation: one epoch from the selected epoch-5 weights, fresh optimizer, learning rate
5e-6, no warmup. Validation increased from 86.50% to 86.74%; training stopped. - On a seeded sample of 5,000 previously seen training examples, F1 was 95.18%. The train–validation gap is consistent with overfitting but does not by itself prove memorization.
The selected KF-DeBERTa model uses a token-classification head. It does not contain the BiLSTM, CRF, FGM, or R-Drop components of the separate KoELECTRA experiments.
Use
from transformers import AutoModelForTokenClassification, AutoTokenizer
model_id = "mrleast/kf-deberta-klue-ner"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForTokenClassification.from_pretrained(model_id)
The standard model.safetensors and tokenizer files support Transformers. best_model.pt contains the same selected weights in the project's original checkpoint format for its character-offset inference code. Its SHA-256 is 8293b8a3fca1feb57952da9b6df8bd52cea893b067e8488f291b72db1aeb4a62. The model weights are excluded from the GitHub source repository because of size.
The source project and detailed audit are at Ezzzzz4/korean_ner. Historical images or subtoken-level scores from that project should not be attributed to this checkpoint.