The open-source model of span-marker-roberta-large-fewnerd-fine-super - Accurately complete fine-grained named entity recognition

Span Marker Roberta Large Fewnerd Fine Super

Developed by tomaarsen

This is a SpanMarker model based on roberta-large, specifically designed for fine-grained named entity recognition tasks, trained on the FewNERD dataset.

Sequence Labeling

Safetensors

Supports Multiple Languages#Fine-grained NER #RoBERTa-enhanced #Multi-category Entity Recognition

Downloads 53

Release Time : 3/30/2023

Model Overview

The model adopts the SpanMarker architecture combined with the roberta-large encoder, capable of identifying various named entities in text, suitable for scenarios such as information extraction.

Model Features

Fine-grained Entity Recognition

Supports recognition of 66 fine-grained entity types, covering multiple domains such as people, places, and organizations.

High-performance Base Model

Based on the roberta-large encoder, providing powerful semantic understanding capabilities.

SpanMarker Architecture

Utilizes the advanced SpanMarker method to effectively address entity boundary recognition issues.

Model Capabilities

Named Entity Recognition

Fine-grained Entity Classification

Text Information Extraction

Use Cases

Information Extraction

News Person Identification

Identify mentioned individuals and their types from news texts.

Can accurately identify person entities such as 'Amelia Earhart'.

Geographic Information Extraction

Identify geographic entities such as places and buildings in text.

Can identify geographic entities like 'Paris' and 'the Atlantic Ocean'.

Content Analysis

Film and TV Show Analysis

Identify movies, TV shows, etc., mentioned in text.

Can accurately identify works such as 'Under Siege'.

🚀 SpanMarker with roberta-large on FewNERD

This is a SpanMarker model trained on the FewNERD dataset for Named Entity Recognition, using roberta-large as the encoder.

🚀 Quick Start

This is a SpanMarker model trained on the FewNERD dataset that can be used for Named Entity Recognition. This SpanMarker model uses roberta-large as the underlying encoder. See train.py for the training script.

✨ Features

Trained on the FewNERD dataset for Named Entity Recognition.
Utilizes roberta-large as the encoder.

📚 Documentation

Model Details

Model Description

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

Uses

Direct Use

💻 Usage Examples

Basic Usage

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-roberta-large-fewnerd-fine-super")
# Run inference
entities = model.predict("Most of the Stev

📄 License

This model is released under the cc-by-sa-4.0 license.

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