SpanMarker with BAAI/bge-base-en-v1.5 on FewNERD
This is a SpanMarker model trained on the FewNERD dataset that can be used for Named Entity Recognition. This SpanMarker model uses BAAI/bge-base-en-v1.5 as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: BAAI/bge-base-en-v1.5
- 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 | "L'Atlantide", "Bosch", "Shawshank Redemption" |
| art-music | "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Hollywood Studio Symphony", "Champion Lover" |
| art-other | "Venus de Milo", "The Today Show", "Aphrodite of Milos" |
| art-painting | "Cofiwch Dryweryn", "Touit", "Production/Reproduction" |
| art-writtenart | "Time", "The Seven Year Itch", "Imelda de ' Lambertazzi" |
| building-airport | "Newark Liberty International Airport", "Luton Airport", "Sheremetyevo International Airport" |
| building-hospital | "Hokkaido University Hospital", "Memorial Sloan-Kettering Cancer Center", "Yeungnam University Hospital" |
| building-hotel | "Radisson Blu Sea Plaza Hotel", "Flamingo Hotel", "The Standard Hotel" |
| building-library | "Bayerische Staatsbibliothek", "British Library", "Berlin State Library" |
| building-other | "Communiplex", "Alpha Recording Studios", "Henry Ford Museum" |
| building-restaurant | "Carnegie Deli", "Fatburger", "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", "Vietnam War", "Easter Offensive" |
| event-disaster | "1693 Sicily earthquake", "the 1912 North Mount Lyell Disaster", "1990s North Korean famine" |
| event-election | "Elections to the European Parliament", "March 1898 elections", "1982 Mitcham and Morden by-election" |
| event-other | "Eastwood Scoring Stage", "Masaryk Democratic Movement", "Union for a Popular Movement" |
| event-protest | "French Revolution", "Iranian Constitutional Revolution", "Russian Revolution" |
| event-sportsevent | "Stanley Cup", "National Champions", "World Cup" |
| location-GPE | "Croatian", "the Republic of Croatia", "Mediterranean Basin" |
| location-bodiesofwater | "Norfolk coast", "Atatürk Dam Lake", "Arthur Kill" |
| location-island | "Staten Island", "Laccadives", "new Samsat district" |
| location-mountain | "Ruweisat Ridge", "Salamander Glacier", "Miteirya Ridge" |
| location-other | "Victoria line", "Northern City Line", "Cartuther" |
| location-park | "Shenandoah National Park", "Gramercy Park", "Painted Desert Community Complex Historic District" |
| location-road/railway/highway/transit | "NJT", "Friern Barnet Road", "Newark-Elizabeth Rail Link" |
| organization-company | "Texas Chicken", "Dixy Chicken", "Church 's Chicken" |
| organization-education | "Barnard College", "MIT", "Belfast Royal Academy and the Ulster College of Physical Education" |
| organization-government/governmentagency | "Diet", "Congregazione dei Nobili", "Supreme Court" |
| organization-media/newspaper | "Clash", "TimeOut Melbourne", "Al Jazeera" |
| organization-other | "Defence Sector C", "IAEA", "4th Army" |
| organization-politicalparty | "Al Wafa ' Islamic", "Kenseitō", "Shimpotō" |
| organization-religion | "Christian", "Jewish", "UPCUSA" |
| organization-showorganization | "Lizzy", "Mr. Mister", "Bochumer Symphoniker" |
| organization-sportsleague | "First Division", "China League One", "NHL" |
| organization-sportsteam | "Arsenal", "Tottenham", "Luc Alphand Aventures" |
| other-astronomything | "Algol", "`` Caput Larvae ''", "Zodiac" |
| other-award | "Grand Commander of the Order of the Niger", "Order of the Republic of Guinea and Nigeria", "GCON" |
| other-biologything | "BAR", "N-terminal lipid", "Amphiphysin" |
| other-chemicalthing | "uranium", "carbon dioxide", "sulfur" |
| other-currency | "lac crore", "$", "Travancore Rupee" |
| other-disease | "French Dysentery Epidemic of 1779", "hypothyroidism", "bladder cancer" |
| other-educationaldegree | "Bachelor", "Master", "BSc ( Hons ) in physics" |
| other-god | "El", "Fujin", "Raijin" |
| other-language | "English", "Latin", "Breton-speaking" |
| other-law | "Thirty Years ' Peace", "Leahy–Smith America Invents Act ( AIA", "United States Freedom Support Act" |
| other-livingthing | "monkeys", "insects", "patchouli" |
| other-medical | "amitriptyline", "Pediatrics", "pediatrician" |
| person-actor | "Ellaline Terriss", "Edmund Payne", "Tchéky Karyo" |
| person-artist/author | "Hicks", "George Axelrod", "Gaetano Donizett" |
| person-athlete | "Jaguar", "Tozawa", "Neville" |
| person-director | "Bob Swaim", "Richard Quine", "Frank Darabont" |
| person-other | "Richard Benson", "Holden", "Campbell" |
| person-politician | "Emeric", "William", "Rivière" |
| person-scholar | "Stalmine", "Wurdack", "Stedman" |
| person-soldier | "Krukenberg", "Joachim Ziegler", "Helmuth Weidling" |
| product-airplane | "EC135T2 CPDS", "Spey-equipped FGR.2s", "Luton" |
| product-car | "100EX", "Phantom", "Corvettes - GT1 C6R" |
| product-food | "red grape", "V. labrusca", "yakiniku" |
| product-game | "Splinter Cell", "Hardcore RPG", "Airforce Delta" |
| product-other | "X11", "PDP-1", "Fairbottom Bobs" |
| product-ship | "HMS `` Chinkara ''", "Essex", "Congress" |
| product-software | "Wikipedia", "AmiPDF", "Apdf" |
| product-train | "55022", "Royal Scots Grey", "High Speed Trains" |
| product-weapon | "ZU-23-2M Wróbel", "ZU-23-2MR Wróbel II", "AR-15 's" |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("guishe/span-marker-bge-base-en-v1.5-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-bge-base-en-v1.5-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-bge-base-en-v1.5-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: 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 Results
| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|---|---|---|---|---|---|---|
| 0.5964 | 3000 | 0.0324 | 0.6263 | 0.5826 | 0.6037 | 0.8981 |
| 1.1928 | 6000 | 0.0278 | 0.6620 | 0.6499 | 0.6559 | 0.9132 |
| 1.7893 | 9000 | 0.0264 | 0.6719 | 0.6614 | 0.6666 | 0.9159 |
| 2.3857 | 12000 | 0.0260 | 0.6724 | 0.6703 | 0.6714 | 0.9174 |
| 2.9821 | 15000 | 0.0258 | 0.6740 | 0.6713 | 0.6726 | 0.9177 |
Framework Versions
- Python: 3.10.8
- SpanMarker: 1.4.0
- 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}
}
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Model tree for guishe/span-marker-bge-base-en-v1.5-fewnerd-fine-super
Base model
BAAI/bge-base-en-v1.5Dataset used to train guishe/span-marker-bge-base-en-v1.5-fewnerd-fine-super
Evaluation results
- F1 on FewNERDself-reported0.673
- Precision on FewNERDself-reported0.674
- Recall on FewNERDself-reported0.671