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---
language:
- en
- multilingual
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:
- precision
- recall
- f1
widget:
- text: "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris."
example_title: "English 1"
- text: The WPC led the international peace movement in the decade after the Second
World War, but its failure to speak out against the Soviet suppression of the
1956 Hungarian uprising and the resumption of Soviet nuclear tests in 1961 marginalised
it, and in the 1960s it was eclipsed by the newer, non-aligned peace organizations
like the Campaign for Nuclear Disarmament.
example_title: "English 2"
- text: Most of the Steven Seagal movie "Under Siege" (co-starring Tommy Lee Jones)
was filmed on the Battleship USS Alabama, which is docked on Mobile Bay at Battleship
Memorial Park and open to the public.
example_title: "English 3"
- text: 'The Central African CFA franc (French: "franc CFA" or simply "franc", ISO
4217 code: XAF) is the currency of six independent states in Central Africa: Cameroon,
Central African Republic, Chad, Republic of the Congo, Equatorial Guinea and Gabon.'
example_title: "English 4"
- text: Brenner conducted post-doctoral research at Brandeis University with Gregory
Petsko and then took his first academic position at Thomas Jefferson University
in 1996, moving to Dartmouth Medical School in 2003, where he served as Associate
Director for Basic Sciences at Norris Cotton Cancer Center.
example_title: "English 5"
- text: On Friday, October 27, 2017, the Senate of Spain (Senado) voted 214 to 47
to invoke Article 155 of the Spanish Constitution over Catalonia after the Catalan
Parliament declared the independence.
example_title: "English 6"
- text: "Amelia Earthart voló su Lockheed Vega 5B monomotor a través del Océano Atlántico hasta París."
example_title: "Spanish"
- text: "Amelia Earthart a fait voler son monomoteur Lockheed Vega 5B à travers l'ocean Atlantique jusqu'à Paris."
example_title: "French"
- text: "Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B über den Atlantik nach Paris."
example_title: "German"
- text: "Амелия Эртхарт перелетела на своем одномоторном самолете Lockheed Vega 5B через Атлантический океан в Париж."
example_title: "Russian"
- text: "Amelia Earthart vloog met haar één-motorige Lockheed Vega 5B over de Atlantische Oceaan naar Parijs."
example_title: "Dutch"
- text: "Amelia Earthart przeleciała swoim jednosilnikowym samolotem Lockheed Vega 5B przez Ocean Atlantycki do Paryża."
example_title: "Polish"
- text: "Amelia Earthart flaug eins hreyfils Lockheed Vega 5B yfir Atlantshafið til Parísar."
example_title: "Icelandic"
- text: "Η Amelia Earthart πέταξε το μονοκινητήριο Lockheed Vega 5B της πέρα από τον Ατλαντικό Ωκεανό στο Παρίσι."
example_title: "Greek"
pipeline_tag: token-classification
co2_eq_emissions:
emissions: 452.84872035276965
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 3.118
hardware_used: 1 x NVIDIA GeForce RTX 3090
base_model: xlm-roberta-base
model-index:
- name: SpanMarker with xlm-roberta-base on FewNERD
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: FewNERD
type: DFKI-SLT/few-nerd
split: test
metrics:
- type: f1
value: 0.6884821229658107
name: F1
- type: precision
value: 0.6890426017339362
name: Precision
- type: recall
value: 0.6879225552622042
name: Recall
---
# SpanMarker with xlm-roberta-base 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 [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder.
## Model Details
### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd)
- **Languages:** en, multilingual
- **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 | "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
```python
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.
<details><summary>Click to expand</summary>
```python
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")
```
</details>
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## 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](https://github.com/mlco2/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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