/SpanMarkerNER

SpanMarker for Named Entity Recognition

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SpanMarker for Named Entity Recognition

🤗 Models | 🛠️ Getting Started In Google Colab | 📄 Documentation | 📊 Thesis

SpanMarker is a framework for training powerful Named Entity Recognition models using familiar encoders such as BERT, RoBERTa and ELECTRA. Built on top of the familiar 🤗 Transformers library, SpanMarker inherits a wide range of powerful functionalities, such as easily loading and saving models, hyperparameter optimization, automatic logging in various tools, checkpointing, callbacks, mixed precision training, 8-bit inference, and more.

Based on the PL-Marker paper, SpanMarker breaks the mold through its accessibility and ease of use. Crucially, SpanMarker works out of the box with many common encoders such as bert-base-cased, roberta-large and bert-base-multilingual-cased, and automatically works with datasets using the IOB, IOB2, BIOES, BILOU or no label annotation scheme.

Additionally, the SpanMarker library has been integrated with the Hugging Face Hub and the Hugging Face Inference API. See the SpanMarker documentation on Hugging Face or see all SpanMarker models on the Hugging Face Hub. Through the Inference API integration, users can test any SpanMarker model on the Hugging Face Hub for free using a widget on the model page. Furthermore, each public SpanMarker model offers a free API for fast prototyping and can be deployed to production using Hugging Face Inference Endpoints.

Inference API Widget (on a model page) Free Inference API (Deploy > Inference API on a model page)
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Documentation

Feel free to have a look at the documentation.

Installation

You may install the span_marker Python module via pip like so:

pip install span_marker

Quick Start

Training

Please have a look at our Getting Started notebook for details on how SpanMarker is commonly used. It explains the following snippet in more detail. Alternatively, have a look at the training scripts that have been successfully used in the past.

Colab Kaggle Gradient Studio Lab
Open In Colab Kaggle Gradient Open In SageMaker Studio Lab 
from pathlib import Path
from datasets import load_dataset
from transformers import TrainingArguments
from span_marker import SpanMarkerModel, Trainer, SpanMarkerModelCardData


def main() -> None:
    # Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
    dataset_id = "DFKI-SLT/few-nerd"
    dataset_name = "FewNERD"
    dataset = load_dataset(dataset_id, "supervised")
    dataset = dataset.remove_columns("ner_tags")
    dataset = dataset.rename_column("fine_ner_tags", "ner_tags")
    labels = dataset["train"].features["ner_tags"].feature.names
    # ['O', 'art-broadcastprogram', 'art-film', 'art-music', 'art-other', ...

    # Initialize a SpanMarker model using a pretrained BERT-style encoder
    encoder_id = "bert-base-cased"
    model_id = f"tomaarsen/span-marker-{encoder_id}-fewnerd-fine-super"
    model = SpanMarkerModel.from_pretrained(
        encoder_id,
        labels=labels,
        # SpanMarker hyperparameters:
        model_max_length=256,
        marker_max_length=128,
        entity_max_length=8,
        # Model card arguments
        model_card_data=SpanMarkerModelCardData(
            model_id=model_id,
            encoder_id=encoder_id,
            dataset_name=dataset_name,
            dataset_id=dataset_id,
            license="cc-by-sa-4.0",
            language="en",
        ),
    )

    # Prepare the 🤗 transformers training arguments
    output_dir = Path("models") / model_id
    args = TrainingArguments(
        output_dir=output_dir,
        # Training Hyperparameters:
        learning_rate=5e-5,
        per_device_train_batch_size=32,
        per_device_eval_batch_size=32,
        num_train_epochs=3,
        weight_decay=0.01,
        warmup_ratio=0.1,
        bf16=True,  # Replace `bf16` with `fp16` if your hardware can't use bf16.
        # Other Training parameters
        logging_first_step=True,
        logging_steps=50,
        evaluation_strategy="steps",
        save_strategy="steps",
        eval_steps=3000,
        save_total_limit=2,
        dataloader_num_workers=2,
    )

    # Initialize the trainer using our model, training args & dataset, and train
    trainer = Trainer(
        model=model,
        args=args,
        train_dataset=dataset["train"],
        eval_dataset=dataset["validation"],
    )
    trainer.train()

    # Compute & save the metrics on the test set
    metrics = trainer.evaluate(dataset["test"], metric_key_prefix="test")
    trainer.save_metrics("test", metrics)

    # Save the final checkpoint
    trainer.save_model(output_dir / "checkpoint-final")

if __name__ == "__main__":
    main()

Inference

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-fewnerd-fine-super")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
[{'span': 'Amelia Earhart', 'label': 'person-other', 'score': 0.7659597396850586, 'char_start_index': 0, 'char_end_index': 14},
 {'span': 'Lockheed Vega 5B', 'label': 'product-airplane', 'score': 0.9725785851478577, 'char_start_index': 38, 'char_end_index': 54},
 {'span': 'Atlantic', 'label': 'location-bodiesofwater', 'score': 0.7587679028511047, 'char_start_index': 66, 'char_end_index': 74},
 {'span': 'Paris', 'label': 'location-GPE', 'score': 0.9892390966415405, 'char_start_index': 78, 'char_end_index': 83}]

Pretrained Models

All models in this list contain train.py files that show the training scripts used to generate them. Additionally, all training scripts used are stored in the training_scripts directory. These trained models have Hosted Inference API widgets that you can use to experiment with the models on their Hugging Face model pages. Additionally, Hugging Face provides each model with a free API (Deploy > Inference API on the model page).

These models are further elaborated on in my thesis.

FewNERD

OntoNotes v5.0

  • tomaarsen/span-marker-roberta-large-ontonotes5 was trained in 3 hours on the OntoNotes v5.0 dataset, reaching a performance of 91.54 F1. For reference, the current strongest spaCy model (en_core_web_trf) reaches 89.8 F1. This SpanMarker model uses a roberta-large encoder under the hood.

CoNLL03

CoNLL++

MultiNERD

Using pretrained SpanMarker models with spaCy

All SpanMarker models on the Hugging Face Hub can also be easily used in spaCy. It's as simple as including 1 line to add the span_marker pipeline. See the Documentation or API Reference for more information.

import spacy

# Load the spaCy model with the span_marker pipeline component
nlp = spacy.load("en_core_web_sm", exclude=["ner"])
nlp.add_pipe("span_marker", config={"model": "tomaarsen/span-marker-roberta-large-ontonotes5"})

# Feed some text through the model to get a spacy Doc
text = """Cleopatra VII, also known as Cleopatra the Great, was the last active ruler of the \
Ptolemaic Kingdom of Egypt. She was born in 69 BCE and ruled Egypt from 51 BCE until her \
death in 30 BCE."""
doc = nlp(text)

# And look at the entities
print([(entity, entity.label_) for entity in doc.ents])
"""
[(Cleopatra VII, "PERSON"), (Cleopatra the Great, "PERSON"), (the Ptolemaic Kingdom of Egypt, "GPE"),
(69 BCE, "DATE"), (Egypt, "GPE"), (51 BCE, "DATE"), (30 BCE, "DATE")]
"""

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Context

Argilla

I have developed this library as a part of my thesis work at Argilla. Feel free to read my finished thesis here in this repository!

Changelog

See CHANGELOG.md for news on all SpanMarker versions.

License

See LICENSE for the current license.