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
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