SpanMarker with xlm-roberta-base on FewNERD
This is a SpanMarker model trained on the FewNERD dataset that can be used for Named Entity Recognition. This SpanMarker model uses xlm-roberta-base as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: xlm-roberta-base
- 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 | "The Gale Storm Show : Oh , Susanna", "Corazones", "Street Cents" |
art-film | "L'Atlantide", "Shawshank Redemption", "Bosch" |
art-music | "Hollywood Studio Symphony", "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Champion Lover" |
art-other | "Venus de Milo", "Aphrodite of Milos", "The Today Show" |
art-painting | "Cofiwch Dryweryn", "Production/Reproduction", "Touit" |
art-writtenart | "The Seven Year Itch", "Time", "Imelda de ' Lambertazzi" |
building-airport | "Newark Liberty International Airport", "Luton Airport", "Sheremetyevo International Airport" |
building-hospital | "Hokkaido University Hospital", "Yeungnam University Hospital", "Memorial Sloan-Kettering Cancer Center" |
building-hotel | "Radisson Blu Sea Plaza Hotel", "The Standard Hotel", "Flamingo Hotel" |
building-library | "British Library", "Berlin State Library", "Bayerische Staatsbibliothek" |
building-other | "Communiplex", "Henry Ford Museum", "Alpha Recording Studios" |
building-restaurant | "Fatburger", "Carnegie Deli", "Trumbull" |
building-sportsfacility | "Boston Garden", "Glenn Warner Soccer Facility", "Sports Center" |
building-theater | "Pittsburgh Civic Light Opera", "National Paris Opera", "Sanders Theatre" |
event-attack/battle/war/militaryconflict | "Jurist", "Easter Offensive", "Vietnam War" |
event-disaster | "1693 Sicily earthquake", "1990s North Korean famine", "the 1912 North Mount Lyell Disaster" |
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 | "Mediterranean Basin", "Croatian", "the Republic of Croatia" |
location-bodiesofwater | "Norfolk coast", "Atatürk Dam Lake", "Arthur Kill" |
location-island | "Laccadives", "Staten Island", "new Samsat district" |
location-mountain | "Ruweisat Ridge", "Miteirya Ridge", "Salamander Glacier" |
location-other | "Victoria line", "Northern City Line", "Cartuther" |
location-park | "Painted Desert Community Complex Historic District", "Shenandoah National Park", "Gramercy Park" |
location-road/railway/highway/transit | "Newark-Elizabeth Rail Link", "NJT", "Friern Barnet Road" |
organization-company | "Church 's Chicken", "Texas Chicken", "Dixy 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 | "TimeOut Melbourne", "Al Jazeera", "Clash" |
organization-other | "IAEA", "4th Army", "Defence Sector C" |
organization-politicalparty | "Al Wafa ' Islamic", "Shimpotō", "Kenseitō" |
organization-religion | "UPCUSA", "Jewish", "Christian" |
organization-showorganization | "Bochumer Symphoniker", "Mr. Mister", "Lizzy" |
organization-sportsleague | "First Division", "NHL", "China League One" |
organization-sportsteam | "Tottenham", "Arsenal", "Luc Alphand Aventures" |
other-astronomything | "Algol", "Zodiac", "`` Caput Larvae ''" |
other-award | "Grand Commander of the Order of the Niger", "Order of the Republic of Guinea and Nigeria", "GCON" |
other-biologything | "Amphiphysin", "BAR", "N-terminal lipid" |
other-chemicalthing | "carbon dioxide", "sulfur", "uranium" |
other-currency | "$", "lac crore", "Travancore Rupee" |
other-disease | "hypothyroidism", "bladder cancer", "French Dysentery Epidemic of 1779" |
other-educationaldegree | "Master", "Bachelor", "BSc ( Hons ) in physics" |
other-god | "El", "Fujin", "Raijin" |
other-language | "Breton-speaking", "Latin", "English" |
other-law | "United States Freedom Support Act", "Thirty Years ' Peace", "Leahy–Smith America Invents Act ( AIA" |
other-livingthing | "insects", "patchouli", "monkeys" |
other-medical | "amitriptyline", "pediatrician", "Pediatrics" |
person-actor | "Tchéky Karyo", "Edmund Payne", "Ellaline Terriss" |
person-artist/author | "George Axelrod", "Hicks", "Gaetano Donizett" |
person-athlete | "Jaguar", "Neville", "Tozawa" |
person-director | "Richard Quine", "Frank Darabont", "Bob Swaim" |
person-other | "Campbell", "Richard Benson", "Holden" |
person-politician | "Rivière", "Emeric", "William" |
person-scholar | "Stedman", "Wurdack", "Stalmine" |
person-soldier | "Joachim Ziegler", "Krukenberg", "Helmuth Weidling" |
product-airplane | "EC135T2 CPDS", "Spey-equipped FGR.2s", "Luton" |
product-car | "Phantom", "Corvettes - GT1 C6R", "100EX" |
product-food | "V. labrusca", "red grape", "yakiniku" |
product-game | "Hardcore RPG", "Airforce Delta", "Splinter Cell" |
product-other | "PDP-1", "Fairbottom Bobs", "X11" |
product-ship | "Essex", "Congress", "HMS `` Chinkara ''" |
product-software | "Wikipedia", "Apdf", "AmiPDF" |
product-train | "55022", "Royal Scots Grey", "High Speed Trains" |
product-weapon | "AR-15 's", "ZU-23-2MR Wróbel II", "ZU-23-2M Wróbel" |
Evaluation
Metrics
Label | Precision | Recall | F1 |
---|---|---|---|
all | 0.6890 | 0.6879 | 0.6885 |
art-broadcastprogram | 0.6 | 0.5771 | 0.5883 |
art-film | 0.7384 | 0.7453 | 0.7419 |
art-music | 0.7930 | 0.7221 | 0.7558 |
art-other | 0.4245 | 0.2900 | 0.3446 |
art-painting | 0.5476 | 0.4035 | 0.4646 |
art-writtenart | 0.6400 | 0.6539 | 0.6469 |
building-airport | 0.8219 | 0.8242 | 0.8230 |
building-hospital | 0.7024 | 0.8104 | 0.7526 |
building-hotel | 0.7175 | 0.7283 | 0.7228 |
building-library | 0.74 | 0.7296 | 0.7348 |
building-other | 0.5828 | 0.5910 | 0.5869 |
building-restaurant | 0.5525 | 0.5216 | 0.5366 |
building-sportsfacility | 0.6187 | 0.7881 | 0.6932 |
building-theater | 0.7067 | 0.7626 | 0.7336 |
event-attack/battle/war/militaryconflict | 0.7544 | 0.7468 | 0.7506 |
event-disaster | 0.5882 | 0.5314 | 0.5584 |
event-election | 0.4167 | 0.2198 | 0.2878 |
event-other | 0.4902 | 0.4042 | 0.4430 |
event-protest | 0.3643 | 0.2831 | 0.3186 |
event-sportsevent | 0.6125 | 0.6239 | 0.6182 |
location-GPE | 0.8102 | 0.8553 | 0.8321 |
location-bodiesofwater | 0.6888 | 0.7725 | 0.7282 |
location-island | 0.7285 | 0.6440 | 0.6836 |
location-mountain | 0.7129 | 0.7327 | 0.7227 |
location-other | 0.4376 | 0.2560 | 0.3231 |
location-park | 0.6991 | 0.6900 | 0.6945 |
location-road/railway/highway/transit | 0.6936 | 0.7259 | 0.7094 |
organization-company | 0.6921 | 0.6912 | 0.6917 |
organization-education | 0.7838 | 0.7963 | 0.7900 |
organization-government/governmentagency | 0.5363 | 0.4394 | 0.4831 |
organization-media/newspaper | 0.6215 | 0.6705 | 0.6451 |
organization-other | 0.5766 | 0.5157 | 0.5444 |
organization-politicalparty | 0.6449 | 0.7324 | 0.6859 |
organization-religion | 0.5139 | 0.6057 | 0.5560 |
organization-showorganization | 0.5620 | 0.5657 | 0.5638 |
organization-sportsleague | 0.6348 | 0.6542 | 0.6443 |
organization-sportsteam | 0.7138 | 0.7566 | 0.7346 |
other-astronomything | 0.7418 | 0.7625 | 0.752 |
other-award | 0.7291 | 0.6736 | 0.7002 |
other-biologything | 0.6735 | 0.6275 | 0.6497 |
other-chemicalthing | 0.6025 | 0.5651 | 0.5832 |
other-currency | 0.6843 | 0.8411 | 0.7546 |
other-disease | 0.6284 | 0.7089 | 0.6662 |
other-educationaldegree | 0.5856 | 0.6033 | 0.5943 |
other-god | 0.6089 | 0.6913 | 0.6475 |
other-language | 0.6608 | 0.7968 | 0.7225 |
other-law | 0.6693 | 0.7246 | 0.6958 |
other-livingthing | 0.6070 | 0.6014 | 0.6042 |
other-medical | 0.5062 | 0.5113 | 0.5088 |
person-actor | 0.8274 | 0.7673 | 0.7962 |
person-artist/author | 0.6761 | 0.7294 | 0.7018 |
person-athlete | 0.8132 | 0.8347 | 0.8238 |
person-director | 0.675 | 0.6823 | 0.6786 |
person-other | 0.6472 | 0.6388 | 0.6429 |
person-politician | 0.6621 | 0.6593 | 0.6607 |
person-scholar | 0.5181 | 0.5007 | 0.5092 |
person-soldier | 0.4750 | 0.5131 | 0.4933 |
product-airplane | 0.6230 | 0.6717 | 0.6464 |
product-car | 0.7293 | 0.7176 | 0.7234 |
product-food | 0.5758 | 0.5185 | 0.5457 |
product-game | 0.7049 | 0.6734 | 0.6888 |
product-other | 0.5477 | 0.4067 | 0.4668 |
product-ship | 0.6247 | 0.6395 | 0.6320 |
product-software | 0.6497 | 0.6760 | 0.6626 |
product-train | 0.5505 | 0.5732 | 0.5616 |
product-weapon | 0.6004 | 0.4744 | 0.5300 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-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-xlm-roberta-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-xlm-roberta-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: 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 |
---|---|---|---|---|---|---|
0.2947 | 3000 | 0.0318 | 0.6058 | 0.5990 | 0.6024 | 0.9020 |
0.5893 | 6000 | 0.0266 | 0.6556 | 0.6679 | 0.6617 | 0.9173 |
0.8840 | 9000 | 0.0250 | 0.6691 | 0.6804 | 0.6747 | 0.9206 |
1.1787 | 12000 | 0.0239 | 0.6865 | 0.6761 | 0.6813 | 0.9212 |
1.4733 | 15000 | 0.0234 | 0.6872 | 0.6812 | 0.6842 | 0.9226 |
1.7680 | 18000 | 0.0231 | 0.6919 | 0.6821 | 0.6870 | 0.9227 |
2.0627 | 21000 | 0.0231 | 0.6909 | 0.6871 | 0.6890 | 0.9233 |
2.3573 | 24000 | 0.0231 | 0.6903 | 0.6875 | 0.6889 | 0.9238 |
2.6520 | 27000 | 0.0229 | 0.6918 | 0.6926 | 0.6922 | 0.9242 |
2.9467 | 30000 | 0.0228 | 0.6927 | 0.6930 | 0.6928 | 0.9243 |
Environmental Impact
Carbon emissions were measured using CodeCarbon.
- Carbon Emitted: 0.453 kg of CO2
- Hours Used: 3.118 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}
}
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Base model
FacebookAI/xlm-roberta-baseDataset used to train tomaarsen/span-marker-xlm-roberta-base-fewnerd-fine-super
Evaluation results
- F1 on FewNERDtest set self-reported0.688
- Precision on FewNERDtest set self-reported0.689
- Recall on FewNERDtest set self-reported0.688