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:
- f1
- recall
- precision
pipeline_tag: token-classification
widget:
- text: >-
Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic
to Paris.
example_title: Amelia Earhart
- text: >-
Leonardo di ser Piero da Vinci painted the Mona Lisa based on Italian
noblewoman Lisa del Giocondo.
example_title: Leonardo da Vinci
base_model: bert-base-cased
model-index:
- name: >-
SpanMarker w. bert-base-cased on finegrained, supervised FewNERD by Tom
Aarsen
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: finegrained, supervised FewNERD
type: DFKI-SLT/few-nerd
config: supervised
split: test
revision: 2e3e727c63604fbfa2ff4cc5055359c84fe5ef2c
metrics:
- type: f1
value: 0.7053
name: F1
- type: precision
value: 0.7101
name: Precision
- type: recall
value: 0.7005
name: Recall
Attention! This is a proof-of-concept model deployed here just for research demonstration. Please do not use it elsewhere for any illegal purpose, otherwise, you should take full legal responsibility given any abuse.
SpanMarker with bert-base-cased on FewNERD
This is a SpanMarker model trained on the FewNERD dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-cased as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: bert-base-cased
- 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", "Corazones", "The Gale Storm Show : Oh , Susanna" |
| art-film | "Bosch", "L'Atlantide", "Shawshank Redemption" |
| art-music | "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Champion Lover", "Hollywood Studio Symphony" |
| art-other | "Aphrodite of Milos", "Venus de Milo", "The Today Show" |
| art-painting | "Production/Reproduction", "Touit", "Cofiwch Dryweryn" |
| art-writtenart | "Imelda de ' Lambertazzi", "Time", "The Seven Year Itch" |
| building-airport | "Luton Airport", "Newark Liberty International Airport", "Sheremetyevo International Airport" |
| building-hospital | "Hokkaido University Hospital", "Yeungnam University Hospital", "Memorial Sloan-Kettering Cancer Center" |
| building-hotel | "The Standard Hotel", "Radisson Blu Sea Plaza Hotel", "Flamingo Hotel" |
| building-library | "British Library", "Berlin State Library", "Bayerische Staatsbibliothek" |
| building-other | "Communiplex", "Alpha Recording Studios", "Henry Ford Museum" |
| building-restaurant | "Fatburger", "Carnegie Deli", "Trumbull" |
| building-sportsfacility | "Glenn Warner Soccer Facility", "Boston Garden", "Sports Center" |
| building-theater | "Pittsburgh Civic Light Opera", "Sanders Theatre", "National Paris Opera" |
| event-attack/battle/war/militaryconflict | "Easter Offensive", "Vietnam War", "Jurist" |
| event-disaster | "the 1912 North Mount Lyell Disaster", "1693 Sicily earthquake", "1990s North Korean famine" |
| event-election | "March 1898 elections", "1982 Mitcham and Morden by-election", "Elections to the European Parliament" |
| event-other | "Eastwood Scoring Stage", "Union for a Popular Movement", "Masaryk Democratic Movement" |
| event-protest | "French Revolution", "Russian Revolution", "Iranian Constitutional Revolution" |
| event-sportsevent | "National Champions", "World Cup", "Stanley Cup" |
| location-GPE | "Mediterranean Basin", "the Republic of Croatia", "Croatian" |
| location-bodiesofwater | "Atatürk Dam Lake", "Norfolk coast", "Arthur Kill" |
| location-island | "Laccadives", "Staten Island", "new Samsat district" |
| location-mountain | "Salamander Glacier", "Miteirya Ridge", "Ruweisat Ridge" |
| location-other | "Northern City Line", "Victoria line", "Cartuther" |
| location-park | "Gramercy Park", "Painted Desert Community Complex Historic District", "Shenandoah National Park" |
| location-road/railway/highway/transit | "Friern Barnet Road", "Newark-Elizabeth Rail Link", "NJT" |
| organization-company | "Dixy Chicken", "Texas 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 | "TimeOut Melbourne", "Clash", "Al Jazeera" |
| organization-other | "Defence Sector C", "IAEA", "4th Army" |
| organization-politicalparty | "Shimpotō", "Al Wafa ' Islamic", "Kenseitō" |
| organization-religion | "Jewish", "Christian", "UPCUSA" |
| organization-showorganization | "Lizzy", "Bochumer Symphoniker", "Mr. Mister" |
| organization-sportsleague | "China League One", "First Division", "NHL" |
| organization-sportsteam | "Tottenham", "Arsenal", "Luc Alphand Aventures" |
| other-astronomything | "Zodiac", "Algol", "`` Caput Larvae ''" |
| other-award | "GCON", "Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger" |
| other-biologything | "N-terminal lipid", "BAR", "Amphiphysin" |
| other-chemicalthing | "uranium", "carbon dioxide", "sulfur" |
| other-currency | "$", "Travancore Rupee", "lac crore" |
| other-disease | "French Dysentery Epidemic of 1779", "hypothyroidism", "bladder cancer" |
| other-educationaldegree | "Master", "Bachelor", "BSc ( Hons ) in physics" |
| other-god | "El", "Fujin", "Raijin" |
| other-language | "Breton-speaking", "English", "Latin" |
| other-law | "Thirty Years ' Peace", "Leahy–Smith America Invents Act ( AIA", "United States Freedom Support Act" |
| other-livingthing | "insects", "monkeys", "patchouli" |
| other-medical | "Pediatrics", "amitriptyline", "pediatrician" |
| person-actor | "Ellaline Terriss", "Tchéky Karyo", "Edmund Payne" |
| person-artist/author | "George Axelrod", "Gaetano Donizett", "Hicks" |
| person-athlete | "Jaguar", "Neville", "Tozawa" |
| person-director | "Bob Swaim", "Richard Quine", "Frank Darabont" |
| person-other | "Richard Benson", "Holden", "Campbell" |
| person-politician | "William", "Rivière", "Emeric" |
| person-scholar | "Stedman", "Wurdack", "Stalmine" |
| person-soldier | "Helmuth Weidling", "Krukenberg", "Joachim Ziegler" |
| product-airplane | "Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS" |
| product-car | "100EX", "Corvettes - GT1 C6R", "Phantom" |
| product-food | "red grape", "yakiniku", "V. labrusca" |
| product-game | "Airforce Delta", "Hardcore RPG", "Splinter Cell" |
| product-other | "Fairbottom Bobs", "X11", "PDP-1" |
| product-ship | "Congress", "Essex", "HMS `` Chinkara ''" |
| product-software | "AmiPDF", "Apdf", "Wikipedia" |
| product-train | "High Speed Trains", "55022", "Royal Scots Grey" |
| product-weapon | "AR-15 's", "ZU-23-2M Wróbel", "ZU-23-2MR Wróbel II" |
Uses
Direct Use
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-fewnerd-fine-super")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
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-bert-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-bert-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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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 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: 1.3.1.dev
- Transformers : 4.29.2
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.3
- Tokenizers: 0.13.2