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:
  - DFKI-SLT/few-nerd
metrics:
  - precision
  - recall
  - f1
widget:
  - text: >-
      Concern and scepticism surround Niger uranium mining waste storage plans.
      Towering mounds dot the desert landscape in northern Niger's Arlit region,
      but they are heaps of partially radioactive waste left from four decades
      of operations at one of the world's biggest uranium mines. An ambitious
      10-year scheme costing $160 million is underway to secure the waste and
      avoid risks to health and the environment, but many local people are
      worried or sceptical. France's nuclear giant Areva, now called Orano,
      worked the area under a subsidiary, the Akouta Mining Company (Cominak).
      Cominak closed the site in 2021 after extracting 75,000 tonnes of uranium,
      much of which went to fuelling the scores of nuclear reactors that provide
      the backbone of France's electricity supply. Cominak's director general
      Mahaman Sani Abdoulaye showcased the rehabilitation project to the first
      French journalists to visit the site since 2010, when seven Areva
      employees were kidnapped by jihadists.
  - text: >-
      SE Michigan counties allege insulin gouging; Localities file lawsuit
      against pharmaceutical makers. Four metro Detroit counties filed federal
      lawsuits Wednesday against some of the nation's biggest pharmaceutical
      manufacturers and pharmacy benefit managers alleging illegal price fixing
      for insulin products. Macomb, Monroe, Wayne and Washtenaw counties filed
      the lawsuits in U.S. District Court in New Jersey against more than a
      dozen companies, including Lilly, Sanofi Aventis, Novo Nordisk, Express
      Scripts, Optum Rx and CVS Caremark, per their attorneys. "These are the
      first such lawsuits that have been filed in the state of Michigan and
      probably more to come," said attorney Melvin Butch Hollowell of the Miller
      Law Firm. He described the allegations during a news conference, saying
      that nationally "the pharmacies and manufacturers get together. They
      control about 90% of the market each, of the insulin market. They talk to
      each other secretly. And they jack up the prices through anticompetitive
      means. And what we've seen is over the past 20 years, when we talk about
      jacking up the prices, they jack them up 1,500% in the last 20 years.
      1,500%."
  - text: >-
      Foreign governments may be spying on your smartphone notifications,
      senator says. Washington (CNN) — Foreign governments have reportedly
      attempted to spy on iPhone and Android users through the mobile app
      notifications they receive on their smartphones - and the US government
      has forced Apple and Google to keep quiet about it, according to a top US
      senator. Through legal demands sent to the tech giants, governments have
      allegedly tried to force Apple and Google to turn over sensitive
      information that could include the contents of a notification - such as
      previews of a text message displayed on a lock screen, or an update about
      app activity, Oregon Democratic Sen. Ron Wyden said in a new report.
      Wyden's report reflects the latest example of long-running tensions
      between tech companies and governments over law enforcement demands, which
      have stretched on for more than a decade. Governments around the world
      have particularly battled with tech companies over encryption, which
      provides critical protections to users and businesses while in some cases
      preventing law enforcement from pursuing investigations into messages sent
      over the internet.
  - text: >-
      Tech giants ‘could severely disable UK spooks from stopping online harms’.
      Silicon Valley tech giants’ actions could “severely disable” UK spooks
      from preventing harm caused by online paedophiles and fraudsters, Suella
      Braverman  has suggested. The Conservative former home secretary named
      Facebook owner Meta , and Apple, and their use of technologies such as
      end-to-end encryption as a threat to attempts to tackle digital crimes.
      She claimed the choice to back these technologies without “safeguards”
      could “enable and indeed facilitate some of the worst atrocities that our
      brave men and women in law enforcement agencies deal with every day”, as
      MPs  began considering changes to investigatory powers laws. The
      Investigatory Powers (Amendment) Bill  includes measures to make it easier
      for agencies to examine and retain bulk datasets, such as publicly
      available online telephone records, and would allow intelligence agencies
      to use internet connection records to aid detection of their targets. We
      know that the terrorists, the serious organised criminals, and fraudsters,
      and the online paedophiles, all take advantage of the dark web and
      encrypted spaces
  - text: >-
      Camargo Corrêa asks Toffoli to suspend the fine agreed with Lava Jato. The
      Camargo Corrêa group has asked Justice Dias Toffoli to suspend the R$1.4
      billion fine it agreed to pay in its leniency agreement under Operation
      Car Wash. The company asked for an extension of the minister's decisions
      that benefited J&F and Odebrecht. Like the other companies, it claimed
      that it suffered undue pressure from members of the Federal Public
      Prosecutor's Office (MPF) to close the deal. Much of the request is based
      on messages exchanged between prosecutors from the Curitiba task force and
      former judge Sergio Moro - Camargo Corrêa requested full access to the
      material, seized in Operation Spoofing, which arrested the hackers who
      broke into cell phones. The dialogues, according to the group's defense,
      indicate that the executives did not freely agree to the deal, since they
      were the targets of lawsuits and pre-trial detentions.
pipeline_tag: token-classification
base_model: numind/generic-entity_recognition_NER-v1
model-index:
  - name: SpanMarker with numind/generic-entity_recognition-v1 on FewNERD
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: FewNERD
          type: DFKI-SLT/few-nerd
          split: eval
        metrics:
          - type: f1
            value: 0.7039859923782059
            name: F1
          - type: precision
            value: 0.7047408904377952
            name: Precision
          - type: recall
            value: 0.7032327098380559
            name: Recall

SpanMarker with numind/generic-entity_recognition-v1 on FewNERD

This is a SpanMarker model trained on the FewNERD dataset that can be used for Named Entity Recognition. This SpanMarker model uses numind/generic-entity_recognition_NER-v1 as the underlying encoder.

Model Details

Model Description

Model Type: SpanMarker
Encoder: numind/generic-entity_recognition_NER-v1
Maximum Sequence Length: 256 tokens
Maximum Entity Length: 8 words
Training Dataset: FewNERD
Language: en
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", "The Gale Storm Show : Oh , Susanna", "Street Cents"
art-film	"Shawshank Redemption", "L'Atlantide", "Bosch"
art-music	"Hollywood Studio Symphony", "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Champion Lover"
art-other	"The Today Show", "Venus de Milo", "Aphrodite of Milos"
art-painting	"Production/Reproduction", "Touit", "Cofiwch Dryweryn"
art-writtenart	"The Seven Year Itch", "Imelda de ' Lambertazzi", "Time"
building-airport	"Sheremetyevo International Airport", "Newark Liberty International Airport", "Luton Airport"
building-hospital	"Yeungnam University Hospital", "Hokkaido University Hospital", "Memorial Sloan-Kettering Cancer Center"
building-hotel	"The Standard Hotel", "Flamingo Hotel", "Radisson Blu Sea Plaza Hotel"
building-library	"British Library", "Bayerische Staatsbibliothek", "Berlin State Library"
building-other	"Henry Ford Museum", "Alpha Recording Studios", "Communiplex"
building-restaurant	"Carnegie Deli", "Fatburger", "Trumbull"
building-sportsfacility	"Boston Garden", "Sports Center", "Glenn Warner Soccer Facility"
building-theater	"Sanders Theatre", "National Paris Opera", "Pittsburgh Civic Light Opera"
event-attack/battle/war/militaryconflict	"Easter Offensive", "Jurist", "Vietnam War"
event-disaster	"the 1912 North Mount Lyell Disaster", "1990s North Korean famine", "1693 Sicily earthquake"
event-election	"Elections to the European Parliament", "March 1898 elections", "1982 Mitcham and Morden by-election"
event-other	"Union for a Popular Movement", "Masaryk Democratic Movement", "Eastwood Scoring Stage"
event-protest	"Iranian Constitutional Revolution", "French Revolution", "Russian Revolution"
event-sportsevent	"World Cup", "National Champions", "Stanley Cup"
location-GPE	"Croatian", "Mediterranean Basin", "the Republic of Croatia"
location-bodiesofwater	"Arthur Kill", "Atatürk Dam Lake", "Norfolk coast"
location-island	"new Samsat district", "Laccadives", "Staten Island"
location-mountain	"Salamander Glacier", "Miteirya Ridge", "Ruweisat Ridge"
location-other	"Victoria line", "Northern City Line", "Cartuther"
location-park	"Painted Desert Community Complex Historic District", "Gramercy Park", "Shenandoah National Park"
location-road/railway/highway/transit	"NJT", "Newark-Elizabeth Rail Link", "Friern Barnet Road"
organization-company	"Texas Chicken", "Dixy Chicken", "Church 's Chicken"
organization-education	"MIT", "Belfast Royal Academy and the Ulster College of Physical Education", "Barnard College"
organization-government/governmentagency	"Congregazione dei Nobili", "Diet", "Supreme Court"
organization-media/newspaper	"Clash", "Al Jazeera", "TimeOut Melbourne"
organization-other	"Defence Sector C", "IAEA", "4th Army"
organization-politicalparty	"Al Wafa ' Islamic", "Shimpotō", "Kenseitō"
organization-religion	"UPCUSA", "Christian", "Jewish"
organization-showorganization	"Lizzy", "Bochumer Symphoniker", "Mr. Mister"
organization-sportsleague	"China League One", "NHL", "First Division"
organization-sportsteam	"Arsenal", "Luc Alphand Aventures", "Tottenham"
other-astronomything	"Algol", "`` Caput Larvae ''", "Zodiac"
other-award	"Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger", "GCON"
other-biologything	"N-terminal lipid", "Amphiphysin", "BAR"
other-chemicalthing	"uranium", "carbon dioxide", "sulfur"
other-currency	"$", "lac crore", "Travancore Rupee"
other-disease	"bladder cancer", "French Dysentery Epidemic of 1779", "hypothyroidism"
other-educationaldegree	"BSc ( Hons ) in physics", "Bachelor", "Master"
other-god	"Raijin", "Fujin", "El"
other-language	"Breton-speaking", "Latin", "English"
other-law	"Leahy–Smith America Invents Act ( AIA", "United States Freedom Support Act", "Thirty Years ' Peace"
other-livingthing	"monkeys", "patchouli", "insects"
other-medical	"amitriptyline", "Pediatrics", "pediatrician"
person-actor	"Tchéky Karyo", "Edmund Payne", "Ellaline Terriss"
person-artist/author	"Hicks", "Gaetano Donizett", "George Axelrod"
person-athlete	"Tozawa", "Neville", "Jaguar"
person-director	"Richard Quine", "Bob Swaim", "Frank Darabont"
person-other	"Campbell", "Holden", "Richard Benson"
person-politician	"William", "Rivière", "Emeric"
person-scholar	"Wurdack", "Stalmine", "Stedman"
person-soldier	"Joachim Ziegler", "Helmuth Weidling", "Krukenberg"
product-airplane	"Spey-equipped FGR.2s", "EC135T2 CPDS", "Luton"
product-car	"Phantom", "100EX", "Corvettes - GT1 C6R"
product-food	"red grape", "yakiniku", "V. labrusca"
product-game	"Hardcore RPG", "Splinter Cell", "Airforce Delta"
product-other	"X11", "PDP-1", "Fairbottom Bobs"
product-ship	"Essex", "Congress", "HMS `` Chinkara ''"
product-software	"AmiPDF", "Wikipedia", "Apdf"
product-train	"55022", "Royal Scots Grey", "High Speed Trains"
product-weapon	"AR-15 's", "ZU-23-2MR Wróbel II", "ZU-23-2M Wróbel"

Uses

Direct Use for Inference

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("guishe/span-marker-generic-ner-v1-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("guishe/span-marker-generic-ner-v1-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("guishe/span-marker-generic-ner-v1-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: 1e-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
0.2980	3000	0.0290	0.6503	0.6402	0.6452	0.9109
0.5961	6000	0.0250	0.6749	0.6794	0.6772	0.9202
0.8941	9000	0.0236	0.6908	0.6871	0.6889	0.9229
1.1921	12000	0.0234	0.6853	0.7007	0.6929	0.9239
1.4902	15000	0.0227	0.6966	0.6929	0.6948	0.9241
1.7882	18000	0.0221	0.7073	0.6922	0.6997	0.9250
2.0862	21000	0.0223	0.7003	0.6993	0.6998	0.9252
2.3843	24000	0.0222	0.6971	0.7027	0.6999	0.9254
2.6823	27000	0.0219	0.7044	0.7004	0.7024	0.9259
2.9803	30000	0.0219	0.7047	0.7032	0.7040	0.9261

Framework Versions

Python: 3.10.8
SpanMarker: 1.5.0
Transformers: 4.28.0
PyTorch: 1.13.1+cu117
Datasets: 2.14.4
Tokenizers: 0.13.3

Citation

BibTeX

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