K-Monitor
/

kmdb_ner_v5

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 ---
+language:
+- hu
+license: apache-2.0
+base_model: SZTAKI-HLT/hubert-base-cc
+pipeline_tag: token-classification
+tags:
+- hungarian
+- ner
+- corruption
+datasets:
+- K-Monitor/kmdb_base
+metrics:
+- precision
+- recall
+- f1
 ---
+# kmdb_ner_v5
+Hungarian named entity recognition for the
+[K-Monitor press database](https://adatbazis.k-monitor.hu/). It does two jobs
+at once: it finds people, organisations and places in an article, and it marks
+which of them are the ones an editor would actually record for that article.
+Labels are `B-/I-` + `{POS,NEG}_{PER,ORG,LOC}`. `POS` means the entity is
+relevant to the article's case and belongs in the database; `NEG` means it is
+merely mentioned.
+Finetuned from `SZTAKI-HLT/hubert-base-cc` on 58,524 articles from
+[`K-Monitor/kmdb_base`](https://huggingface.co/datasets/K-Monitor/kmdb_base):
+spaCy (`hu_core_news_lg`) supplies the entity spans, and an entity is labelled
+`POS` when it matches one of the names an editor tagged on that article.
+## Results
+Entity-level seqeval scores on 4,161 held-out articles. The honest comparison
+against the previous `boapps/kmdb_ner_model` is the second column: that
+checkpoint was trained with an unseeded split, so most of the general test set
+was in its training data, whereas these articles were added to the database
+after it was published.
+| model | test F1 | F1 on articles the old model never saw |
+| --- | --- | --- |
+| `boapps/kmdb_ner_model` | 0.8244\* | 0.6684 |
+| **`kmdb_ner_v5`** | 0.8171 | **0.7299** |
+\* inflated by train/test leakage in the earlier checkpoint.
+Per class, on those unseen articles:
+| label | precision | recall | F1 | previous F1 |
+| --- | --- | --- | --- | --- |
+| NEG_LOC | 0.7817 | 0.7075 | 0.7427 | 0.7208 |
+| NEG_ORG | 0.7530 | 0.7587 | 0.7558 | 0.7176 |
+| NEG_PER | 0.7298 | 0.6752 | 0.7014 | 0.6505 |
+| POS_LOC | 0.6219 | 0.7128 | 0.6642 | 0.5729 |
+| POS_ORG | 0.6469 | 0.7130 | 0.6784 | 0.5270 |
+| POS_PER | 0.6971 | 0.8029 | 0.7463 | 0.6484 |
+| **micro avg** | 0.7242 | 0.7356 | **0.7299** | 0.6684 |
+The largest gain is on `POS_ORG` (0.5270 → 0.6784): the previous training data
+matched gold names by exact substring, so tagged organisations whose database
+name carries a suffix — `Hunguest Hotels Zrt.`, `Honvédelmi Minisztérium (HM)` —
+were labelled `NEG` and the model learned to ignore them.
+## Usage
+```python
+from transformers import AutoTokenizer, pipeline
+classifier = pipeline(
+    "ner",
+    model="K-Monitor/kmdb_ner_v5",
+    aggregation_strategy="first",
+    stride=8,
+    tokenizer=AutoTokenizer.from_pretrained(
+        "SZTAKI-HLT/hubert-base-cc", model_max_length=512
+    ),
+)
+classifier(article_text)
+```
+Adjacent fragments of one entity have to be merged afterwards (huBERT splits
+detached Hungarian suffixes, e.g. `Budapest` + `-en`); when merging, keep the
+entity type and treat the result as `POS` if either fragment is `POS`. The
+reference implementation is `join_entities` in
+[`sajtoadatbazis-automat`](https://github.com/k-monitor/sajtoadatbazis-automat)
+(`webapp/auto_kmdb/processors/NERProcessor.py`).
+## Limitations
+The `POS`/`NEG` supervision is derived automatically by matching spaCy entities
+against editor-tagged names, so it inherits spaCy's boundary errors and the
+matching heuristic's mistakes; it is not hand-annotated. Scores on articles
+from after the training window (~0.73 F1) are well below in-distribution scores
+(~0.82), so expect the real-world quality to track the former.