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: >-
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.
- text: >-
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.
- 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.
- 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.
- 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.
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}
}