Hugging Face's logo

SDVM
/
multilingual-ner-refined

Token Classification
Safetensors
German
xlm-roberta
ner
named-entity-recognition
sdvm
refined
Eval Results (legacy)
Model card Files
xet
 Community
multilingual-ner-refined / README.md
philippds's picture
philippds
Add model card
e1a1325 verified
preview code
|
Raw
History Blame Contribute Delete
2.71 kB
metadata 
license: cc-by-sa-4.0
language:
  - de
tags:
  - ner
  - named-entity-recognition
  - xlm-roberta
  - token-classification
  - sdvm
  - refined
datasets:
  - SDVM/xtreme-PAN-X.de
metrics:
  - f1
pipeline_tag: token-classification
model-index:
  - name: multilingual-ner-refined
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          type: SDVM/xtreme-PAN-X.de
          name: PAN-X.de (Refined)
          config: default
          split: test
        metrics:
          - type: f1
            name: F1
            value: 0.87

SDVM Multilingual NER — Refined

An XLM-RoBERTa-base model fine-tuned for Named Entity Recognition on the refined (cleaned) PAN-X.de dataset from the XTREME benchmark.

This model is part of a paired experiment by SDVM to demonstrate the impact of data quality on NER performance. Compare with SDVM/multilingual-ner-original, which was trained on uncleaned data.

Training Details

  • Base model: xlm-roberta-base
  • Dataset: SDVM/xtreme-PAN-X.de — tokens_refined and ner_tags_refined columns (cleaned)
  • Training: 3 epochs, batch size 8, learning rate 2e-5, weight decay 0.01
  • Task: Token classification with IOB2 tags

Data Refinement

The refined dataset had ~8.5% Wikipedia markup noise removed:

  • Bold/italic markers (**, '')
  • Template and link brackets ({{, }}, [[, ]])
  • Section headers (==, ===)
  • German Wikipedia redirect tokens (WEITERLEITUNG)
  • Embedded markup stripped from tokens
  • B-/I- tag continuity repaired after token removal

Labels

ID Tag
0 O
1 B-PER
2 I-PER
3 B-ORG
4 I-ORG
5 B-LOC
6 I-LOC

Usage 

from transformers import pipeline

ner = pipeline("token-classification", model="SDVM/multilingual-ner-refined")
result = ner("Angela Merkel wurde in Hamburg geboren.")
print(result)

Context

By removing Wikipedia markup artifacts from the training data, this model learns cleaner token representations and produces more reliable entity predictions. Compare its F1 score with the original model to see the impact of data refinement.

Reference