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tomaarsen
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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
Carbon Emissions
Model card Files Metrics Community
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  1. README.md +369 -0
  2. added_tokens.json +4 -0
  3. config.json +234 -0
  4. pytorch_model.bin +3 -0
  5. special_tokens_map.json +7 -0
  6. tokenizer.json +0 -0
  7. tokenizer_config.json +16 -0
  8. vocab.txt +0 -0
README.md ADDED
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+ ---
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+ language:
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+ - en
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+ - multilingual
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+ license: cc-by-sa-4.0
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+ library_name: span-marker
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+ tags:
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+ - span-marker
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+ - token-classification
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+ - ner
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+ - named-entity-recognition
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+ - generated_from_span_marker_trainer
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+ datasets:
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+ - DFKI-SLT/few-nerd
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ widget:
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+ - text: The WPC led the international peace movement in the decade after the Second
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+ World War, but its failure to speak out against the Soviet suppression of the
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+ 1956 Hungarian uprising and the resumption of Soviet nuclear tests in 1961 marginalised
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+ it, and in the 1960s it was eclipsed by the newer, non-aligned peace organizations
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+ like the Campaign for Nuclear Disarmament.
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+ - text: Most of the Steven Seagal movie "Under Siege "(co-starring Tommy Lee Jones)
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+ was filmed on the, which is docked on Mobile Bay at Battleship Memorial Park and
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+ open to the public.
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+ - text: 'The Central African CFA franc (French: "franc CFA "or simply "franc ", ISO
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+ 4217 code: XAF) is the currency of six independent states in Central Africa: Cameroon,
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+ Central African Republic, Chad, Republic of the Congo, Equatorial Guinea and Gabon.'
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+ - text: Brenner conducted post-doctoral research at Brandeis University with Gregory
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+ Petsko and then took his first academic position at Thomas Jefferson University
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+ in 1996, moving to Dartmouth Medical School in 2003, where he served as Associate
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+ Director for Basic Sciences at Norris Cotton Cancer Center.
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+ - text: On Friday, October 27, 2017, the Senate of Spain (Senado) voted 214 to 47
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+ to invoke Article 155 of the Spanish Constitution over Catalonia after the Catalan
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+ Parliament declared the independence.
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+ pipeline_tag: token-classification
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+ co2_eq_emissions:
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+ emissions: 572.6675932546113
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+ source: codecarbon
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+ training_type: fine-tuning
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+ on_cloud: false
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+ cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
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+ ram_total_size: 31.777088165283203
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+ hours_used: 3.867
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+ hardware_used: 1 x NVIDIA GeForce RTX 3090
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+ base_model: bert-base-multilingual-cased
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+ model-index:
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+ - name: SpanMarker with bert-base-multilingual-cased on FewNERD
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+ results:
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+ - task:
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+ type: token-classification
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+ name: Named Entity Recognition
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+ dataset:
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+ name: FewNERD
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+ type: DFKI-SLT/few-nerd
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+ split: test
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+ metrics:
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+ - type: f1
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+ value: 0.7006507253689264
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+ name: F1
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+ - type: precision
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+ value: 0.7040676584045078
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+ name: Precision
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+ - type: recall
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+ value: 0.6972667978051558
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+ name: Recall
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+ ---
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+
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+ # SpanMarker with bert-base-multilingual-cased on FewNERD
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+
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+ This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) as the underlying encoder.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SpanMarker
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+ - **Encoder:** [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
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+ - **Maximum Sequence Length:** 256 tokens
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+ - **Maximum Entity Length:** 8 words
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+ - **Training Dataset:** [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd)
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+ - **Languages:** en, multilingual
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+ - **License:** cc-by-sa-4.0
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+
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+ ### Model Sources
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+
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+ - **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
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+ - **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:-----------------------------------------|:---------------------------------------------------------------------------------------------------------|
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+ | art-broadcastprogram | "Corazones", "Street Cents", "The Gale Storm Show : Oh , Susanna" |
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+ | art-film | "L'Atlantide", "Bosch", "Shawshank Redemption" |
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+ | art-music | "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Hollywood Studio Symphony", "Champion Lover" |
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+ | art-other | "Aphrodite of Milos", "The Today Show", "Venus de Milo" |
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+ | art-painting | "Production/Reproduction", "Touit", "Cofiwch Dryweryn" |
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+ | art-writtenart | "The Seven Year Itch", "Time", "Imelda de ' Lambertazzi" |
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+ | building-airport | "Luton Airport", "Newark Liberty International Airport", "Sheremetyevo International Airport" |
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+ | building-hospital | "Hokkaido University Hospital", "Yeungnam University Hospital", "Memorial Sloan-Kettering Cancer Center" |
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+ | building-hotel | "Flamingo Hotel", "The Standard Hotel", "Radisson Blu Sea Plaza Hotel" |
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+ | building-library | "British Library", "Bayerische Staatsbibliothek", "Berlin State Library" |
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+ | building-other | "Communiplex", "Henry Ford Museum", "Alpha Recording Studios" |
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+ | building-restaurant | "Fatburger", "Carnegie Deli", "Trumbull" |
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+ | building-sportsfacility | "Sports Center", "Glenn Warner Soccer Facility", "Boston Garden" |
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+ | building-theater | "Sanders Theatre", "Pittsburgh Civic Light Opera", "National Paris Opera" |
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+ | event-attack/battle/war/militaryconflict | "Vietnam War", "Jurist", "Easter Offensive" |
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+ | event-disaster | "1693 Sicily earthquake", "the 1912 North Mount Lyell Disaster", "1990s North Korean famine" |
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+ | event-election | "March 1898 elections", "1982 Mitcham and Morden by-election", "Elections to the European Parliament" |
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+ | event-other | "Eastwood Scoring Stage", "Masaryk Democratic Movement", "Union for a Popular Movement" |
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+ | event-protest | "Russian Revolution", "Iranian Constitutional Revolution", "French Revolution" |
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+ | event-sportsevent | "Stanley Cup", "World Cup", "National Champions" |
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+ | location-GPE | "Mediterranean Basin", "Croatian", "the Republic of Croatia" |
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+ | location-bodiesofwater | "Norfolk coast", "Atatürk Dam Lake", "Arthur Kill" |
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+ | location-island | "Staten Island", "Laccadives", "new Samsat district" |
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+ | location-mountain | "Miteirya Ridge", "Ruweisat Ridge", "Salamander Glacier" |
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+ | location-other | "Victoria line", "Cartuther", "Northern City Line" |
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+ | location-park | "Painted Desert Community Complex Historic District", "Shenandoah National Park", "Gramercy Park" |
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+ | location-road/railway/highway/transit | "Friern Barnet Road", "Newark-Elizabeth Rail Link", "NJT" |
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+ | organization-company | "Church 's Chicken", "Dixy Chicken", "Texas Chicken" |
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+ | organization-education | "MIT", "Barnard College", "Belfast Royal Academy and the Ulster College of Physical Education" |
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+ | organization-government/governmentagency | "Supreme Court", "Diet", "Congregazione dei Nobili" |
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+ | organization-media/newspaper | "TimeOut Melbourne", "Clash", "Al Jazeera" |
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+ | organization-other | "IAEA", "Defence Sector C", "4th Army" |
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+ | organization-politicalparty | "Al Wafa ' Islamic", "Kenseitō", "Shimpotō" |
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+ | organization-religion | "Christian", "UPCUSA", "Jewish" |
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+ | organization-showorganization | "Lizzy", "Mr. Mister", "Bochumer Symphoniker" |
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+ | organization-sportsleague | "China League One", "NHL", "First Division" |
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+ | organization-sportsteam | "Luc Alphand Aventures", "Tottenham", "Arsenal" |
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+ | other-astronomything | "`` Caput Larvae ''", "Algol", "Zodiac" |
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+ | other-award | "GCON", "Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger" |
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+ | other-biologything | "BAR", "Amphiphysin", "N-terminal lipid" |
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+ | other-chemicalthing | "sulfur", "uranium", "carbon dioxide" |
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+ | other-currency | "Travancore Rupee", "$", "lac crore" |
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+ | other-disease | "bladder cancer", "hypothyroidism", "French Dysentery Epidemic of 1779" |
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+ | other-educationaldegree | "Master", "Bachelor", "BSc ( Hons ) in physics" |
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+ | other-god | "Fujin", "Raijin", "El" |
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+ | other-language | "Latin", "English", "Breton-speaking" |
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+ | other-law | "Thirty Years ' Peace", "United States Freedom Support Act", "Leahy–Smith America Invents Act ( AIA" |
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+ | other-livingthing | "monkeys", "insects", "patchouli" |
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+ | other-medical | "Pediatrics", "amitriptyline", "pediatrician" |
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+ | person-actor | "Edmund Payne", "Ellaline Terriss", "Tchéky Karyo" |
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+ | person-artist/author | "George Axelrod", "Hicks", "Gaetano Donizett" |
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+ | person-athlete | "Tozawa", "Neville", "Jaguar" |
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+ | person-director | "Richard Quine", "Frank Darabont", "Bob Swaim" |
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+ | person-other | "Richard Benson", "Campbell", "Holden" |
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+ | person-politician | "Rivière", "William", "Emeric" |
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+ | person-scholar | "Wurdack", "Stedman", "Stalmine" |
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+ | person-soldier | "Joachim Ziegler", "Krukenberg", "Helmuth Weidling" |
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+ | product-airplane | "Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS" |
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+ | product-car | "Corvettes - GT1 C6R", "Phantom", "100EX" |
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+ | product-food | "V. labrusca", "yakiniku", "red grape" |
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+ | product-game | "Airforce Delta", "Hardcore RPG", "Splinter Cell" |
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+ | product-other | "PDP-1", "Fairbottom Bobs", "X11" |
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+ | product-ship | "HMS `` Chinkara ''", "Congress", "Essex" |
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+ | product-software | "Apdf", "Wikipedia", "AmiPDF" |
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+ | product-train | "Royal Scots Grey", "High Speed Trains", "55022" |
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+ | product-weapon | "AR-15 's", "ZU-23-2M Wróbel", "ZU-23-2MR Wróbel II" |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Precision | Recall | F1 |
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+ |:-----------------------------------------|:----------|:-------|:-------|
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+ | **all** | 0.7041 | 0.6973 | 0.7007 |
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+ | art-broadcastprogram | 0.5863 | 0.6252 | 0.6051 |
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+ | art-film | 0.7779 | 0.752 | 0.7647 |
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+ | art-music | 0.8014 | 0.7570 | 0.7786 |
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+ | art-other | 0.4209 | 0.3221 | 0.3649 |
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+ | art-painting | 0.5938 | 0.6667 | 0.6281 |
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+ | art-writtenart | 0.6854 | 0.6415 | 0.6628 |
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+ | building-airport | 0.8197 | 0.8242 | 0.8219 |
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+ | building-hospital | 0.7215 | 0.8187 | 0.7671 |
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+ | building-hotel | 0.7233 | 0.6906 | 0.7066 |
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+ | building-library | 0.7588 | 0.7268 | 0.7424 |
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+ | building-other | 0.5842 | 0.5855 | 0.5848 |
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+ | building-restaurant | 0.5567 | 0.4871 | 0.5195 |
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+ | building-sportsfacility | 0.6512 | 0.7690 | 0.7052 |
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+ | building-theater | 0.6994 | 0.7516 | 0.7246 |
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+ | event-attack/battle/war/militaryconflict | 0.7800 | 0.7332 | 0.7559 |
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+ | event-disaster | 0.5767 | 0.5266 | 0.5505 |
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+ | event-election | 0.5106 | 0.1319 | 0.2096 |
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+ | event-other | 0.4931 | 0.4145 | 0.4504 |
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+ | event-protest | 0.3711 | 0.4337 | 0.4000 |
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+ | event-sportsevent | 0.6156 | 0.6156 | 0.6156 |
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+ | location-GPE | 0.8175 | 0.8508 | 0.8338 |
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+ | location-bodiesofwater | 0.7297 | 0.7622 | 0.7456 |
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+ | location-island | 0.7314 | 0.6703 | 0.6995 |
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+ | location-mountain | 0.7538 | 0.7283 | 0.7409 |
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+ | location-other | 0.4370 | 0.3040 | 0.3585 |
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+ | location-park | 0.7063 | 0.6878 | 0.6969 |
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+ | location-road/railway/highway/transit | 0.7092 | 0.7259 | 0.7174 |
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+ | organization-company | 0.6911 | 0.6943 | 0.6927 |
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+ | organization-education | 0.7799 | 0.7973 | 0.7885 |
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+ | organization-government/governmentagency | 0.5518 | 0.4474 | 0.4942 |
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+ | organization-media/newspaper | 0.6268 | 0.6761 | 0.6505 |
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+ | organization-other | 0.5804 | 0.5341 | 0.5563 |
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+ | organization-politicalparty | 0.6627 | 0.7306 | 0.6949 |
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+ | organization-religion | 0.5636 | 0.6265 | 0.5934 |
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+ | organization-showorganization | 0.6023 | 0.6086 | 0.6054 |
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+ | organization-sportsleague | 0.6594 | 0.6497 | 0.6545 |
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+ | organization-sportsteam | 0.7341 | 0.7703 | 0.7518 |
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+ | other-astronomything | 0.7806 | 0.8289 | 0.8040 |
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+ | other-award | 0.7230 | 0.6703 | 0.6957 |
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+ | other-biologything | 0.6733 | 0.6366 | 0.6544 |
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+ | other-chemicalthing | 0.5962 | 0.5838 | 0.5899 |
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+ | other-currency | 0.7135 | 0.7822 | 0.7463 |
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+ | other-disease | 0.6260 | 0.7063 | 0.6637 |
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+ | other-educationaldegree | 0.6 | 0.6033 | 0.6016 |
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+ | other-god | 0.7051 | 0.7118 | 0.7085 |
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+ | other-language | 0.6849 | 0.7968 | 0.7366 |
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+ | other-law | 0.6814 | 0.6843 | 0.6829 |
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+ | other-livingthing | 0.5959 | 0.6443 | 0.6192 |
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+ | other-medical | 0.5247 | 0.4811 | 0.5020 |
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+ | person-actor | 0.8342 | 0.7960 | 0.8146 |
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+ | person-artist/author | 0.7052 | 0.7482 | 0.7261 |
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+ | person-athlete | 0.8396 | 0.8530 | 0.8462 |
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+ | person-director | 0.725 | 0.7329 | 0.7289 |
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+ | person-other | 0.6866 | 0.6672 | 0.6767 |
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+ | person-politician | 0.6819 | 0.6852 | 0.6835 |
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+ | person-scholar | 0.5468 | 0.4953 | 0.5198 |
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+ | person-soldier | 0.5360 | 0.5641 | 0.5497 |
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+ | product-airplane | 0.6825 | 0.6730 | 0.6777 |
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+ | product-car | 0.7205 | 0.7016 | 0.7109 |
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+ | product-food | 0.6036 | 0.5394 | 0.5697 |
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+ | product-game | 0.7740 | 0.6876 | 0.7282 |
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+ | product-other | 0.5250 | 0.4117 | 0.4615 |
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+ | product-ship | 0.6781 | 0.6763 | 0.6772 |
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+ | product-software | 0.6701 | 0.6603 | 0.6652 |
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+ | product-train | 0.5919 | 0.6051 | 0.5984 |
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+ | product-weapon | 0.6507 | 0.5433 | 0.5921 |
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+
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+ ## Uses
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+
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+ ### Direct Use for Inference
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+
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+ ```python
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+ from span_marker import SpanMarkerModel
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+
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+ # Download from the 🤗 Hub
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+ model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-fewnerd-fine-super")
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+ # Run inference
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+ 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.")
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+ ```
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+
247
+ ### Downstream Use
248
+ You can finetune this model on your own dataset.
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+
250
+ <details><summary>Click to expand</summary>
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+
252
+ ```python
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+ from span_marker import SpanMarkerModel, Trainer
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+
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+ # Download from the 🤗 Hub
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+ model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-fewnerd-fine-super")
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+
258
+ # Specify a Dataset with "tokens" and "ner_tag" columns
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+ dataset = load_dataset("conll2003") # For example CoNLL2003
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+
261
+ # Initialize a Trainer using the pretrained model & dataset
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+ trainer = Trainer(
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+ model=model,
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+ train_dataset=dataset["train"],
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+ eval_dataset=dataset["validation"],
266
+ )
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+ trainer.train()
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+ trainer.save_model("tomaarsen/span-marker-mbert-base-fewnerd-fine-super-finetuned")
269
+ ```
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+ </details>
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+
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+ <!--
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+ ### Out-of-Scope Use
274
+
275
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
276
+ -->
277
+
278
+ <!--
279
+ ## Bias, Risks and Limitations
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+
281
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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+ -->
283
+
284
+ <!--
285
+ ### Recommendations
286
+
287
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
288
+ -->
289
+
290
+ ## Training Details
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+
292
+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:----------------------|:----|:--------|:----|
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+ | Sentence length | 1 | 24.4945 | 267 |
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+ | Entities per sentence | 0 | 2.5832 | 88 |
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+
298
+ ### Training Hyperparameters
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
305
+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 3
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+
308
+ ### Training Results
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+ | Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
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+ |:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
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+ | 0.2972 | 3000 | 0.0274 | 0.6488 | 0.6457 | 0.6473 | 0.9121 |
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+ | 0.5944 | 6000 | 0.0252 | 0.6686 | 0.6545 | 0.6615 | 0.9160 |
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+ | 0.8915 | 9000 | 0.0239 | 0.6918 | 0.6547 | 0.6727 | 0.9178 |
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+ | 1.1887 | 12000 | 0.0235 | 0.6962 | 0.6727 | 0.6842 | 0.9210 |
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+ | 1.4859 | 15000 | 0.0233 | 0.6872 | 0.6742 | 0.6806 | 0.9201 |
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+ | 1.7831 | 18000 | 0.0226 | 0.6969 | 0.6891 | 0.6929 | 0.9236 |
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+ | 2.0802 | 21000 | 0.0231 | 0.7030 | 0.6916 | 0.6973 | 0.9246 |
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+ | 2.3774 | 24000 | 0.0227 | 0.7020 | 0.6936 | 0.6978 | 0.9248 |
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+ | 2.6746 | 27000 | 0.0223 | 0.7079 | 0.6989 | 0.7034 | 0.9258 |
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+ | 2.9718 | 30000 | 0.0222 | 0.7089 | 0.7009 | 0.7049 | 0.9263 |
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+
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+ ### Environmental Impact
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+ Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
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+ - **Carbon Emitted**: 0.573 kg of CO2
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+ - **Hours Used**: 3.867 hours
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+
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+ ### Training Hardware
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+ - **On Cloud**: No
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+ - **GPU Model**: 1 x NVIDIA GeForce RTX 3090
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+ - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
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+ - **RAM Size**: 31.78 GB
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+
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+ ### Framework Versions
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+ - Python: 3.9.16
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+ - SpanMarker: 1.4.1.dev
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+ - Transformers: 4.30.0
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+ - PyTorch: 2.0.1+cu118
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+ - Datasets: 2.14.0
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+ - Tokenizers: 0.13.2
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+
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+ ## Citation
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+
343
+ ### BibTeX
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+ ```
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+ @software{Aarsen_SpanMarker,
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+ author = {Aarsen, Tom},
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+ license = {Apache-2.0},
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+ title = {{SpanMarker for Named Entity Recognition}},
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+ url = {https://github.com/tomaarsen/SpanMarkerNER}
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+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
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+ ## Model Card Contact
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+
368
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