Using SpanMarker at Hugging Face

SpanMarker is a framework for training powerful Named Entity Recognition models using familiar encoders such as BERT, RoBERTa and DeBERTa. Tightly implemented on top of the 🤗 Transformers library, SpanMarker can take good advantage of it. As a result, SpanMarker will be intuitive to use for anyone familiar with Transformers.

Exploring SpanMarker in the Hub

You can find span_marker models by filtering at the left of the models page.

All models on the Hub come with these useful features:

  1. An automatically generated model card with a brief description.
  2. An interactive widget you can use to play with the model directly in the browser.
  3. An Inference API that allows you to make inference requests.

Installation

To get started, you can follow the SpanMarker installation guide. You can also use the following one-line install through pip:

pip install -U span_marker

Using existing models

All span_marker models can easily be loaded from the Hub.

from span_marker import SpanMarkerModel

model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-fewnerd-fine-super")

Once loaded, you can use SpanMarkerModel.predict to perform inference.

model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
[
    {"span": "Amelia Earhart", "label": "person-other", "score": 0.7629689574241638, "char_start_index": 0, "char_end_index": 14},
    {"span": "Lockheed Vega 5B", "label": "product-airplane", "score": 0.9833564758300781, "char_start_index": 38, "char_end_index": 54},
    {"span": "Atlantic", "label": "location-bodiesofwater", "score": 0.7621214389801025, "char_start_index": 66, "char_end_index": 74},
    {"span": "Paris", "label": "location-GPE", "score": 0.9807717204093933, "char_start_index": 78, "char_end_index": 83}
]

If you want to load a specific SpanMarker model, you can click Use in SpanMarker and you will be given a working snippet!

Additional resources

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