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span-marker-mbert-base-fewnerd-fine-super

Token Classification
SpanMarker
PyTorch
TensorBoard
English
multilingual
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results
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SpanMarker with bert-base-multilingual-cased on FewNERD

This is a SpanMarker model trained on the FewNERD dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-multilingual-cased as the underlying encoder.

Model Details

Model Description

  • Model Type: SpanMarker
  • Encoder: bert-base-multilingual-cased
  • Maximum Sequence Length: 256 tokens
  • Maximum Entity Length: 8 words
  • Training Dataset: FewNERD
  • Languages: en, multilingual
  • License: cc-by-sa-4.0

Model Sources

Model Labels

Label Examples
art-broadcastprogram "Corazones", "Street Cents", "The Gale Storm Show : Oh , Susanna"
art-film "L'Atlantide", "Bosch", "Shawshank Redemption"
art-music "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Hollywood Studio Symphony", "Champion Lover"
art-other "Aphrodite of Milos", "The Today Show", "Venus de Milo"
art-painting "Production/Reproduction", "Touit", "Cofiwch Dryweryn"
art-writtenart "The Seven Year Itch", "Time", "Imelda de ' Lambertazzi"
building-airport "Luton Airport", "Newark Liberty International Airport", "Sheremetyevo International Airport"
building-hospital "Hokkaido University Hospital", "Yeungnam University Hospital", "Memorial Sloan-Kettering Cancer Center"
building-hotel "Flamingo Hotel", "The Standard Hotel", "Radisson Blu Sea Plaza Hotel"
building-library "British Library", "Bayerische Staatsbibliothek", "Berlin State Library"
building-other "Communiplex", "Henry Ford Museum", "Alpha Recording Studios"
building-restaurant "Fatburger", "Carnegie Deli", "Trumbull"
building-sportsfacility "Sports Center", "Glenn Warner Soccer Facility", "Boston Garden"
building-theater "Sanders Theatre", "Pittsburgh Civic Light Opera", "National Paris Opera"
event-attack/battle/war/militaryconflict "Vietnam War", "Jurist", "Easter Offensive"
event-disaster "1693 Sicily earthquake", "the 1912 North Mount Lyell Disaster", "1990s North Korean famine"
event-election "March 1898 elections", "1982 Mitcham and Morden by-election", "Elections to the European Parliament"
event-other "Eastwood Scoring Stage", "Masaryk Democratic Movement", "Union for a Popular Movement"
event-protest "Russian Revolution", "Iranian Constitutional Revolution", "French Revolution"
event-sportsevent "Stanley Cup", "World Cup", "National Champions"
location-GPE "Mediterranean Basin", "Croatian", "the Republic of Croatia"
location-bodiesofwater "Norfolk coast", "Atatürk Dam Lake", "Arthur Kill"
location-island "Staten Island", "Laccadives", "new Samsat district"
location-mountain "Miteirya Ridge", "Ruweisat Ridge", "Salamander Glacier"
location-other "Victoria line", "Cartuther", "Northern City Line"
location-park "Painted Desert Community Complex Historic District", "Shenandoah National Park", "Gramercy Park"
location-road/railway/highway/transit "Friern Barnet Road", "Newark-Elizabeth Rail Link", "NJT"
organization-company "Church 's Chicken", "Dixy Chicken", "Texas Chicken"
organization-education "MIT", "Barnard College", "Belfast Royal Academy and the Ulster College of Physical Education"
organization-government/governmentagency "Supreme Court", "Diet", "Congregazione dei Nobili"
organization-media/newspaper "TimeOut Melbourne", "Clash", "Al Jazeera"
organization-other "IAEA", "Defence Sector C", "4th Army"
organization-politicalparty "Al Wafa ' Islamic", "Kenseitō", "Shimpotō"
organization-religion "Christian", "UPCUSA", "Jewish"
organization-showorganization "Lizzy", "Mr. Mister", "Bochumer Symphoniker"
organization-sportsleague "China League One", "NHL", "First Division"
organization-sportsteam "Luc Alphand Aventures", "Tottenham", "Arsenal"
other-astronomything "`` Caput Larvae ''", "Algol", "Zodiac"
other-award "GCON", "Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger"
other-biologything "BAR", "Amphiphysin", "N-terminal lipid"
other-chemicalthing "sulfur", "uranium", "carbon dioxide"
other-currency "Travancore Rupee", "$", "lac crore"
other-disease "bladder cancer", "hypothyroidism", "French Dysentery Epidemic of 1779"
other-educationaldegree "Master", "Bachelor", "BSc ( Hons ) in physics"
other-god "Fujin", "Raijin", "El"
other-language "Latin", "English", "Breton-speaking"
other-law "Thirty Years ' Peace", "United States Freedom Support Act", "Leahy–Smith America Invents Act ( AIA"
other-livingthing "monkeys", "insects", "patchouli"
other-medical "Pediatrics", "amitriptyline", "pediatrician"
person-actor "Edmund Payne", "Ellaline Terriss", "Tchéky Karyo"
person-artist/author "George Axelrod", "Hicks", "Gaetano Donizett"
person-athlete "Tozawa", "Neville", "Jaguar"
person-director "Richard Quine", "Frank Darabont", "Bob Swaim"
person-other "Richard Benson", "Campbell", "Holden"
person-politician "Rivière", "William", "Emeric"
person-scholar "Wurdack", "Stedman", "Stalmine"
person-soldier "Joachim Ziegler", "Krukenberg", "Helmuth Weidling"
product-airplane "Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS"
product-car "Corvettes - GT1 C6R", "Phantom", "100EX"
product-food "V. labrusca", "yakiniku", "red grape"
product-game "Airforce Delta", "Hardcore RPG", "Splinter Cell"
product-other "PDP-1", "Fairbottom Bobs", "X11"
product-ship "HMS `` Chinkara ''", "Congress", "Essex"
product-software "Apdf", "Wikipedia", "AmiPDF"
product-train "Royal Scots Grey", "High Speed Trains", "55022"
product-weapon "AR-15 's", "ZU-23-2M Wróbel", "ZU-23-2MR Wróbel II"

Evaluation

Metrics

Label Precision Recall F1
all 0.7041 0.6973 0.7007
art-broadcastprogram 0.5863 0.6252 0.6051
art-film 0.7779 0.752 0.7647
art-music 0.8014 0.7570 0.7786
art-other 0.4209 0.3221 0.3649
art-painting 0.5938 0.6667 0.6281
art-writtenart 0.6854 0.6415 0.6628
building-airport 0.8197 0.8242 0.8219
building-hospital 0.7215 0.8187 0.7671
building-hotel 0.7233 0.6906 0.7066
building-library 0.7588 0.7268 0.7424
building-other 0.5842 0.5855 0.5848
building-restaurant 0.5567 0.4871 0.5195
building-sportsfacility 0.6512 0.7690 0.7052
building-theater 0.6994 0.7516 0.7246
event-attack/battle/war/militaryconflict 0.7800 0.7332 0.7559
event-disaster 0.5767 0.5266 0.5505
event-election 0.5106 0.1319 0.2096
event-other 0.4931 0.4145 0.4504
event-protest 0.3711 0.4337 0.4000
event-sportsevent 0.6156 0.6156 0.6156
location-GPE 0.8175 0.8508 0.8338
location-bodiesofwater 0.7297 0.7622 0.7456
location-island 0.7314 0.6703 0.6995
location-mountain 0.7538 0.7283 0.7409
location-other 0.4370 0.3040 0.3585
location-park 0.7063 0.6878 0.6969
location-road/railway/highway/transit 0.7092 0.7259 0.7174
organization-company 0.6911 0.6943 0.6927
organization-education 0.7799 0.7973 0.7885
organization-government/governmentagency 0.5518 0.4474 0.4942
organization-media/newspaper 0.6268 0.6761 0.6505
organization-other 0.5804 0.5341 0.5563
organization-politicalparty 0.6627 0.7306 0.6949
organization-religion 0.5636 0.6265 0.5934
organization-showorganization 0.6023 0.6086 0.6054
organization-sportsleague 0.6594 0.6497 0.6545
organization-sportsteam 0.7341 0.7703 0.7518
other-astronomything 0.7806 0.8289 0.8040
other-award 0.7230 0.6703 0.6957
other-biologything 0.6733 0.6366 0.6544
other-chemicalthing 0.5962 0.5838 0.5899
other-currency 0.7135 0.7822 0.7463
other-disease 0.6260 0.7063 0.6637
other-educationaldegree 0.6 0.6033 0.6016
other-god 0.7051 0.7118 0.7085
other-language 0.6849 0.7968 0.7366
other-law 0.6814 0.6843 0.6829
other-livingthing 0.5959 0.6443 0.6192
other-medical 0.5247 0.4811 0.5020
person-actor 0.8342 0.7960 0.8146
person-artist/author 0.7052 0.7482 0.7261
person-athlete 0.8396 0.8530 0.8462
person-director 0.725 0.7329 0.7289
person-other 0.6866 0.6672 0.6767
person-politician 0.6819 0.6852 0.6835
person-scholar 0.5468 0.4953 0.5198
person-soldier 0.5360 0.5641 0.5497
product-airplane 0.6825 0.6730 0.6777
product-car 0.7205 0.7016 0.7109
product-food 0.6036 0.5394 0.5697
product-game 0.7740 0.6876 0.7282
product-other 0.5250 0.4117 0.4615
product-ship 0.6781 0.6763 0.6772
product-software 0.6701 0.6603 0.6652
product-train 0.5919 0.6051 0.5984
product-weapon 0.6507 0.5433 0.5921

Uses

Direct Use for Inference 

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-fewnerd-fine-super")
# Run inference
entities = model.predict("Most of the Steven Seagal movie \"Under Siege \"(co-starring Tommy Lee Jones) was filmed on the, which is docked on Mobile Bay at Battleship Memorial Park and open to the public.")

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("tomaarsen/span-marker-mbert-base-fewnerd-fine-super")

# 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("tomaarsen/span-marker-mbert-base-fewnerd-fine-super-finetuned")

Training Details

Training Set Metrics

Training set Min Median Max
Sentence length 1 24.4945 267
Entities per sentence 0 2.5832 88

Training Hyperparameters

  • learning_rate: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 3

Training Results

Epoch Step Validation Loss Validation Precision Validation Recall Validation F1 Validation Accuracy
0.2972 3000 0.0274 0.6488 0.6457 0.6473 0.9121
0.5944 6000 0.0252 0.6686 0.6545 0.6615 0.9160
0.8915 9000 0.0239 0.6918 0.6547 0.6727 0.9178
1.1887 12000 0.0235 0.6962 0.6727 0.6842 0.9210
1.4859 15000 0.0233 0.6872 0.6742 0.6806 0.9201
1.7831 18000 0.0226 0.6969 0.6891 0.6929 0.9236
2.0802 21000 0.0231 0.7030 0.6916 0.6973 0.9246
2.3774 24000 0.0227 0.7020 0.6936 0.6978 0.9248
2.6746 27000 0.0223 0.7079 0.6989 0.7034 0.9258
2.9718 30000 0.0222 0.7089 0.7009 0.7049 0.9263

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Carbon Emitted: 0.573 kg of CO2
  • Hours Used: 3.867 hours

Training Hardware

  • On Cloud: No
  • GPU Model: 1 x NVIDIA GeForce RTX 3090
  • CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
  • RAM Size: 31.78 GB

Framework Versions

  • Python: 3.9.16
  • SpanMarker: 1.4.1.dev
  • Transformers: 4.30.0
  • PyTorch: 2.0.1+cu118
  • Datasets: 2.14.0
  • Tokenizers: 0.13.2

Citation

BibTeX 

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