Rishabh157/spanmarker-wikiann-mdeberta

Multilingual SpanMarker NER: mDeBERTa-v3 on WikiANN (7 Languages)

Developed by: Rishabh Kumar
Model Name: Rishabh157/spanmarker-wikiann-mdeberta
Base Architecture: microsoft/mdeberta-v3-base

This repository provides an enterprise-grade, state-of-the-art multilingual Named Entity Recognition (NER) model developed by Rishabh Kumar, combining microsoft/mdeberta-v3-base with the SpanMarker (PL-Marker) span-classification framework, fine-tuned across 7 major languages from WikiANN for 3 full epochs (26,934 steps).

Model Overview

• Developer / Creator: Rishabh Kumar (@Rishabh157)
• Base Architecture: microsoft/mdeberta-v3-base (12 layers, 768 hidden dimension, disentangled relative positional attention).
• Task Formulation: SpanMarker (Candidate phrase classification with boundary marker tokens [S_i, E_j] and Greedy Non-Maximum Suppression).
• Tuning Strategy: Full-Parameter Fine-Tuning (all 278.8M parameters active).
• Supported Languages: German (de), English (en), Spanish (es), French (fr), Italian (it), Portuguese (pt), Swedish (sv).
• Entity Taxonomy:
  • PER (Person)
  • ORG (Organization)
  • LOC (Location / Geopolitical Entity)

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Empirical Benchmark Performance

Evaluated on the full official multilingual WikiANN validation set (70,000 sentences across all 7 languages, 10,000 sentences per language; 93,466 total gold entities) using strict exact-span seqeval matching:

Overall 3-Epoch Validation Results

Metric	Score	Notes
Overall F1 Score	76.75%	+15.13 point gain over Epoch 1 (61.62%)
Extraction Precision	88.85%	Consistently high precision anchor, zero false-alarm hallucinations
Extraction Recall	67.55%	Surged from 48.49% to 67.55% via multi-epoch statistical exposure
Sequence Accuracy	87.37%	Exact token and span boundary classification
Validation Loss	0.0421	Steady monotonic decrease across all 3 epochs

Per-Class Performance Breakdown (70,000 Sentences, 93,466 Entities)

Entity Type	Semantic Category	Precision	Recall	F1 Score	Support (# Entities)
PER	Person / Names	92.06%	73.07%	81.47%	31,416
LOC	Location / Geopolitical	88.73%	65.09%	75.09%	33,627
ORG	Organization	85.26%	64.36%	73.35%	28,423
Overall	Micro-Average	88.85%	67.55%	76.75%	93,466

Note on "Support (# Entities)": In Named Entity Recognition benchmarks, Support indicates the exact number of ground-truth entity spans belonging to that category in the validation dataset (70,000 sentences). It defines the sample size over which Recall and F1 are computed, providing transparency into entity frequency across the corpus.

Epoch-by-Epoch Convergence Trajectory

Epoch	Step	Eval Loss	Precision	Recall	F1 Score	Accuracy
1.0	8,978	0.0677	84.50%	48.49%	61.62%	80.38%
2.0	17,956	0.0490	87.77%	61.86%	72.57%	84.64%
3.0	26,934	0.0421	88.85%	67.55%	76.75%	87.37%

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Quickstart & Inference

from span_marker import SpanMarkerModel

# Load model directly from Hugging Face Hub
model = SpanMarkerModel.from_pretrained("Rishabh157/spanmarker-wikiann-mdeberta")

# Multilingual inference examples
sentences = [
    "Katia and Maurice Krafft died at Mount Unzen in Japan in 1991.",
    "An der Yale University sind der Benny Goodman Nachlass und in Harvard der von Eubie Blake.",
    "Barra do Garças Futebol Clube é um clube brasileiro de futebol da cidade de Barra do Garças."
]

predictions = model.predict(sentences)
for sentence, preds in zip(sentences, predictions):
    print(f"\nSentence: {sentence}")
    for entity in preds:
        print(f"  - [{entity['label']}] '{entity['span']}' (char {entity['char_start_index']}:{entity['char_end_index']}, score: {entity['score']:.3f})")

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Training Configuration

• Batch Size: 16 per device (effective batch size 16)
• Steps: 26,934 steps (3 full epochs, ~4.17 hours runtime)
• Precision: FP16 mixed precision
• Sequence Length: 256 tokens max length, 16 words max entity span
• Optimizer: AdamW (lr=3.5e-5, cosine schedule, linear warmup)

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Author & Citation

This model was trained, evaluated, and published by Rishabh Kumar (@Rishabh157).

If you use this model or refer to this work, please cite:

@misc{rishabhkumar2026_spanmarker_wikiann,
  author = {Rishabh Kumar},
  title = {SpanMarker Multilingual NER with mDeBERTa-v3 on WikiANN},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Rishabh157/spanmarker-wikiann-mdeberta}}
}