README.md

metadata

language:
  - en
  - multilingual
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:
  - DFKI-SLT/few-nerd
metrics:
  - precision
  - recall
  - f1
widget:
  - text: >-
      Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic
      to Paris.
    example_title: English 1
  - text: >-
      The WPC led the international peace movement in the decade after the
      Second World War, but its failure to speak out against the Soviet
      suppression of the 1956 Hungarian uprising and the resumption of Soviet
      nuclear tests in 1961 marginalised it, and in the 1960s it was eclipsed by
      the newer, non-aligned peace organizations like the Campaign for Nuclear
      Disarmament.
    example_title: English 2
  - text: >-
      Most of the Steven Seagal movie "Under Siege" (co-starring Tommy Lee
      Jones) was filmed on the Battleship USS Alabama, which is docked on Mobile
      Bay at Battleship Memorial Park and open to the public.
    example_title: English 3
  - text: >-
      The Central African CFA franc (French: "franc CFA" or simply "franc", ISO
      4217 code: XAF) is the currency of six independent states in Central
      Africa: Cameroon, Central African Republic, Chad, Republic of the Congo,
      Equatorial Guinea and Gabon.
    example_title: English 4
  - text: >-
      Brenner conducted post-doctoral research at Brandeis University with
      Gregory Petsko and then took his first academic position at Thomas
      Jefferson University in 1996, moving to Dartmouth Medical School in 2003,
      where he served as Associate Director for Basic Sciences at Norris Cotton
      Cancer Center.
    example_title: English 5
  - text: >-
      On Friday, October 27, 2017, the Senate of Spain (Senado) voted 214 to 47
      to invoke Article 155 of the Spanish Constitution over Catalonia after the
      Catalan Parliament declared the independence.
    example_title: English 6
  - text: >-
      Amelia Earthart voló su Lockheed Vega 5B monomotor a través del Océano
      Atlántico hasta París.
    example_title: Spanish
  - 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
pipeline_tag: token-classification
co2_eq_emissions:
  emissions: 572.6675932546113
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 3.867
  hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: bert-base-multilingual-cased
model-index:
  - name: SpanMarker with bert-base-multilingual-cased on FewNERD
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: FewNERD
          type: DFKI-SLT/few-nerd
          split: test
        metrics:
          - type: f1
            value: 0.7006507253689264
            name: F1
          - type: precision
            value: 0.7040676584045078
            name: Precision
          - type: recall
            value: 0.6972667978051558
            name: Recall

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

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

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}
}