| --- |
| 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: 0.870 |
| --- |
| |
| # 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](https://huggingface.co/SDVM) to demonstrate the impact of data quality on NER performance. Compare with [SDVM/multilingual-ner-original](https://huggingface.co/SDVM/multilingual-ner-original), which was trained on uncleaned data. |
|
|
| ## Training Details |
|
|
| - **Base model**: [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) |
| - **Dataset**: [SDVM/xtreme-PAN-X.de](https://huggingface.co/datasets/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 |
|
|
| ```python |
| 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](https://huggingface.co/SDVM/multilingual-ner-original) to see the impact of data refinement. |
|
|
| ## Reference |
|
|
| - Based on Chapter 4 of [Natural Language Processing with Transformers](https://github.com/nlp-with-transformers/notebooks/blob/main/04_multilingual-ner.ipynb) |
| - Part of the [SDVM](https://huggingface.co/SDVM) data quality demonstration series |
|
|
