metadata

language:
  - en
license: cc-by-sa-4.0
library_name: span-marker
tags:
  - span-marker
  - token-classification
  - ner
  - named-entity-recognition
  - generated_from_span_marker_trainer
datasets:
  - tomaarsen/ner-orgs
metrics:
  - precision
  - recall
  - f1
widget:
  - text: >-
      De Napoli played for FC Luzern in the second half of the 2005–06 Swiss
      Super League campaign, scoring five times in fifteen games and helping
      Luzern to promotion from the Swiss Challenge League.
  - text: >-
      The issue continued to simmer while full-communion agreements with the
      Presbyterian Church USA, Reformed Church in America, United Church of
      Christ, and Episcopal Church (United States) were debated and adopted in
      1997 and 1999.
  - text: >-
      Rune Gerhardsen (born 13 June 1946) is a Norwegian politician,
      representing the Norwegian Labour Party and a former sports leader at
      Norwegian Skating Association representing from Aktiv SK.
  - text: >-
      Konstantin Vladimirovich Pushkaryov (; born February 12, 1985) is a
      Kazakhstani professional ice hockey winger who is currently playing with
      HK Kurbads of the Latvian Hockey League (LAT).
  - text: >-
      SCL claims that its methodology has been approved or endorsed by agencies
      of the Government of the United Kingdom and the Federal government of the
      United States, among others.
pipeline_tag: token-classification
base_model: microsoft/xtremedistil-l12-h384-uncased
model-index:
  - name: >-
      SpanMarker with microsoft/xtremedistil-l12-h384-uncased on FewNERD,
      CoNLL2003, and OntoNotes v5
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: FewNERD, CoNLL2003, and OntoNotes v5
          type: tomaarsen/ner-orgs
          split: test
        metrics:
          - type: f1
            value: 0.7558602090122487
            name: F1
          - type: precision
            value: 0.7620428694430598
            name: Precision
          - type: recall
            value: 0.749777064383806
            name: Recall

SpanMarker with microsoft/xtremedistil-l12-h384-uncased on FewNERD, CoNLL2003, and OntoNotes v5

This is a SpanMarker model trained on the FewNERD, CoNLL2003, and OntoNotes v5 dataset that can be used for Named Entity Recognition. This SpanMarker model uses microsoft/xtremedistil-l12-h384-uncased as the underlying encoder.

Model Details

Model Description

Model Type: SpanMarker
Encoder: microsoft/xtremedistil-l12-h384-uncased
Maximum Sequence Length: 256 tokens
Maximum Entity Length: 8 words
Training Dataset: FewNERD, CoNLL2003, and OntoNotes v5
Language: en
License: cc-by-sa-4.0

Model Sources

Repository: SpanMarker on GitHub
Thesis: SpanMarker For Named Entity Recognition

Model Labels

Label	Examples
ORG	"Texas Chicken", "IAEA", "Church 's Chicken"

Evaluation

Metrics

Label	Precision	Recall	F1
all	0.7620	0.7498	0.7559
ORG	0.7620	0.7498	0.7559

Uses

Direct Use for Inference

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("nbroad/span-marker-xdistil-l12-h384-orgs-v3")
# Run inference
entities = model.predict("SCL claims that its methodology has been approved or endorsed by agencies of the Government of the United Kingdom and the Federal government of the United States, among others.")

Downstream Use

You can finetune this model on your own dataset.

Click to expand

from span_marker import SpanMarkerModel, Trainer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("nbroad/span-marker-xdistil-l12-h384-orgs-v3")

# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003

# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
    model=model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("nbroad/span-marker-xdistil-l12-h384-orgs-v3-finetuned")

Training Details

Training Set Metrics

Training set	Min	Median	Max
Sentence length	1	23.5706	263
Entities per sentence	0	0.7865	39

Training Hyperparameters

learning_rate: 0.0003
train_batch_size: 128
eval_batch_size: 128
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.05
num_epochs: 3
mixed_precision_training: Native AMP

Training Results

Epoch	Step	Validation Loss	Validation Precision	Validation Recall	Validation F1	Validation Accuracy
0.5720	600	0.0086	0.7150	0.7095	0.7122	0.9660
1.1439	1200	0.0074	0.7556	0.7253	0.7401	0.9682
1.7159	1800	0.0073	0.7482	0.7619	0.7550	0.9702
2.2879	2400	0.0072	0.7761	0.7573	0.7666	0.9713
2.8599	3000	0.0070	0.7691	0.7688	0.7689	0.9720

Framework Versions

Python: 3.10.12
SpanMarker: 1.5.0
Transformers: 4.35.2
PyTorch: 2.1.0a0+32f93b1
Datasets: 2.15.0
Tokenizers: 0.15.0

Citation

BibTeX

@software{Aarsen_SpanMarker,
    author = {Aarsen, Tom},
    license = {Apache-2.0},
    title = {{SpanMarker for Named Entity Recognition}},
    url = {https://github.com/tomaarsen/SpanMarkerNER}
}