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---
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 da Vinci painted the Mona Lisa based on Italian noblewoman
Lisa del Giocondo.
example_title: Leonardo da Vinci
- text: Most of the Steven Seagal movie ``Under Siege`` (co-starring Tommy Lee Jones)
was filmed aboard the Battleship USS Alabama, which is docked on Mobile Bay at
Battleship Memorial Park and open to the public.
example_title: Under Siege
base_model: roberta-large
model-index:
- name: SpanMarker w. roberta-large 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: 0.7103
name: F1
- type: precision
value: 0.7136
name: Precision
- type: recall
value: 0.707
name: Recall
---
# SpanMarker with roberta-large on FewNERD
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [roberta-large](https://huggingface.co/roberta-large) as the underlying encoder. See [train.py](train.py) for the training script.
## Model Details
### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [roberta-large](https://huggingface.co/roberta-large)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd)
- **Language:** en
- **License:** cc-by-sa-4.0
### Model Sources
- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
### Model Labels
| Label | Examples |
|:-----------------------------------------|:---------------------------------------------------------------------------------------------------------|
| art-broadcastprogram | "Street Cents", "The Gale Storm Show : Oh , Susanna", "Corazones" |
| art-film | "Shawshank Redemption", "Bosch", "L'Atlantide" |
| art-music | "Hollywood Studio Symphony", "Champion Lover", "Atkinson , Danko and Ford ( with Brockie and Hilton )" |
| art-other | "Aphrodite of Milos", "Venus de Milo", "The Today Show" |
| art-painting | "Production/Reproduction", "Cofiwch Dryweryn", "Touit" |
| art-writtenart | "Imelda de ' Lambertazzi", "Time", "The Seven Year Itch" |
| building-airport | "Sheremetyevo International Airport", "Newark Liberty International Airport", "Luton Airport" |
| building-hospital | "Memorial Sloan-Kettering Cancer Center", "Hokkaido University Hospital", "Yeungnam University Hospital" |
| building-hotel | "Flamingo Hotel", "The Standard Hotel", "Radisson Blu Sea Plaza Hotel" |
| building-library | "British Library", "Berlin State Library", "Bayerische Staatsbibliothek" |
| building-other | "Alpha Recording Studios", "Henry Ford Museum", "Communiplex" |
| building-restaurant | "Fatburger", "Carnegie Deli", "Trumbull" |
| building-sportsfacility | "Sports Center", "Glenn Warner Soccer Facility", "Boston Garden" |
| building-theater | "Pittsburgh Civic Light Opera", "National Paris Opera", "Sanders Theatre" |
| event-attack/battle/war/militaryconflict | "Jurist", "Vietnam War", "Easter Offensive" |
| event-disaster | "the 1912 North Mount Lyell Disaster", "1990s North Korean famine", "1693 Sicily earthquake" |
| 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 | "Croatian", "the Republic of Croatia", "Mediterranean Basin" |
| location-bodiesofwater | "Arthur Kill", "Norfolk coast", "Atatürk Dam Lake" |
| location-island | "new Samsat district", "Staten Island", "Laccadives" |
| location-mountain | "Ruweisat Ridge", "Salamander Glacier", "Miteirya Ridge" |
| location-other | "Northern City Line", "Victoria line", "Cartuther" |
| location-park | "Gramercy Park", "Shenandoah National Park", "Painted Desert Community Complex Historic District" |
| location-road/railway/highway/transit | "NJT", "Friern Barnet Road", "Newark-Elizabeth Rail Link" |
| organization-company | "Church 's Chicken", "Dixy Chicken", "Texas Chicken" |
| organization-education | "MIT", "Barnard College", "Belfast Royal Academy and the Ulster College of Physical Education" |
| organization-government/governmentagency | "Supreme Court", "Congregazione dei Nobili", "Diet" |
| organization-media/newspaper | "Al Jazeera", "Clash", "TimeOut Melbourne" |
| organization-other | "IAEA", "4th Army", "Defence Sector C" |
| organization-politicalparty | "Al Wafa ' Islamic", "Kenseitō", "Shimpotō" |
| organization-religion | "Jewish", "UPCUSA", "Christian" |
| organization-showorganization | "Mr. Mister", "Lizzy", "Bochumer Symphoniker" |
| organization-sportsleague | "China League One", "NHL", "First Division" |
| organization-sportsteam | "Arsenal", "Luc Alphand Aventures", "Tottenham" |
| other-astronomything | "Algol", "`` Caput Larvae ''", "Zodiac" |
| other-award | "GCON", "Grand Commander of the Order of the Niger", "Order of the Republic of Guinea and Nigeria" |
| other-biologything | "BAR", "N-terminal lipid", "Amphiphysin" |
| other-chemicalthing | "carbon dioxide", "sulfur", "uranium" |
| other-currency | "$", "Travancore Rupee", "lac crore" |
| other-disease | "bladder cancer", "French Dysentery Epidemic of 1779", "hypothyroidism" |
| other-educationaldegree | "Bachelor", "Master", "BSc ( Hons ) in physics" |
| other-god | "El", "Fujin", "Raijin" |
| other-language | "Latin", "Breton-speaking", "English" |
| other-law | "Leahy–Smith America Invents Act ( AIA", "Thirty Years ' Peace", "United States Freedom Support Act" |
| other-livingthing | "monkeys", "patchouli", "insects" |
| other-medical | "Pediatrics", "pediatrician", "amitriptyline" |
| person-actor | "Tchéky Karyo", "Ellaline Terriss", "Edmund Payne" |
| person-artist/author | "George Axelrod", "Gaetano Donizett", "Hicks" |
| person-athlete | "Jaguar", "Tozawa", "Neville" |
| person-director | "Bob Swaim", "Frank Darabont", "Richard Quine" |
| person-other | "Richard Benson", "Holden", "Campbell" |
| person-politician | "Emeric", "Rivière", "William" |
| person-scholar | "Stalmine", "Stedman", "Wurdack" |
| person-soldier | "Helmuth Weidling", "Joachim Ziegler", "Krukenberg" |
| product-airplane | "Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS" |
| product-car | "100EX", "Phantom", "Corvettes - GT1 C6R" |
| product-food | "red grape", "yakiniku", "V. labrusca" |
| product-game | "Airforce Delta", "Splinter Cell", "Hardcore RPG" |
| product-other | "Fairbottom Bobs", "X11", "PDP-1" |
| product-ship | "HMS `` Chinkara ''", "Congress", "Essex" |
| product-software | "Wikipedia", "Apdf", "AmiPDF" |
| product-train | "Royal Scots Grey", "High Speed Trains", "55022" |
| product-weapon | "AR-15 's", "ZU-23-2M Wróbel", "ZU-23-2MR Wróbel II" |
## Uses
### Direct Use
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-roberta-large-fewnerd-fine-super")
# Run inference
entities = model.predict("Most of the Steven Seagal movie ``Under Siege`` (co-starring Tommy Lee Jones) was filmed aboard the Battleship USS Alabama, 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.
<details><summary>Click to expand</summary>
```python
from span_marker import SpanMarkerModel, Trainer
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-roberta-large-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-roberta-large-fewnerd-fine-super-finetuned")
```
</details>
## 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: 8
- eval_batch_size: 8
- 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 |