---

license: cc-by-nc-sa-4.0 library_name: span-marker tags:

• span-marker
• token-classification
• ner
• named-entity-recognition pipeline_tag: token-classification widget:
• text: "Amelia Earthart voló su Lockheed Vega 5B monomotor a través del Océano Atlántico hasta París ." example_title: "Spanish"
• text: "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris ." example_title: "English"
• text: "Amelia Earthart a fait voler son monomoteur Lockheed Vega 5B à travers l' ocean Atlantique jusqu'à Paris ." example_title: "French"
• text: "Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B über den Atlantik nach Paris ." example_title: "German"
• text: "Амелия Эртхарт перелетела на своем одномоторном самолете Lockheed Vega 5B через Атлантический океан в Париж ." example_title: "Russian"
• text: "Amelia Earthart vloog met haar één-motorige Lockheed Vega 5B over de Atlantische Oceaan naar Parijs ." example_title: "Dutch"
• text: "Amelia Earthart przeleciała swoim jednosilnikowym samolotem Lockheed Vega 5B przez Ocean Atlantycki do Paryża ." example_title: "Polish"
• text: "Amelia Earthart flaug eins hreyfils Lockheed Vega 5B yfir Atlantshafið til Parísar ." example_title: "Icelandic"
• text: "Η Amelia Earthart πέταξε το μονοκινητήριο Lockheed Vega 5B της πέρα ​​από τον Ατλαντικό Ωκεανό στο Παρίσι ." example_title: "Greek" model-index:
  • name: SpanMarker w. xlm-roberta-base on MultiNERD by Tom Aarsen results:
    • task: type: token-classification name: Named Entity Recognition dataset: type: Babelscape/multinerd name: MultiNERD split: test revision: 2814b78e7af4b5a1f1886fe7ad49632de4d9dd25 metrics:
      • type: f1 value: 0.91314 name: F1
      • type: precision value: 0.91994 name: Precision
      • type: recall value: 0.90643 name: Recall datasets:
  • Babelscape/multinerd language:
  • multilingual metrics:
  • f1
  • recall
  • precision

---

SpanMarker for Named Entity Recognition

Note: Due to major tokenization limitations, this model is deprecated in favor of the much superior tomaarsen/span-marker-mbert-base-multinerd model.

This is a SpanMarker model that can be used for multilingual Named Entity Recognition trained on the MultiNERD dataset. In particular, this SpanMarker model uses xlm-roberta-base as the underlying encoder. See train.py for the training script.

Metrics

Label set

Usage

To use this model for inference, first install the span_marker library:

pip install span_marker

You can then run inference with this model like so:

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-base-multinerd")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")

See the SpanMarker repository for documentation and additional information on this library.

Contributions

Many thanks to Simone Tedeschi from Babelscape for his insight when training this model and his involvement in the creation of the training dataset.