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span-marker-bert-base-multilingual-cased-multinerd

Token Classification
SpanMarker
PyTorch
Generated from Trainer
ner
named-entity-recognition
Eval Results
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span-marker-bert-base-multilingual-cased-multinerd / README.md
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metadata
tags:
  - generated_from_trainer
  - ner
  - named-entity-recognition
  - span-marker
model-index:
  - name: span-marker-bert-base-multilingual-cased-multinerd
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          type: Babelscape/multinerd
          name: MultiNERD
          split: test
          revision: 2814b78e7af4b5a1f1886fe7ad49632de4d9dd25
        metrics:
          - type: f1
            value: 0.927
            name: F1
          - type: precision
            value: 0.9281
            name: Precision
          - type: recall
            value: 0.9259
            name: Recall
license: apache-2.0
datasets:
  - Babelscape/multinerd
metrics:
  - precision
  - recall
  - f1
pipeline_tag: token-classification
widget:
  - text: >-
      Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B über den
      Atlantik nach Paris.
    example_title: German
  - text: >-
      Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic
      to Paris.
    example_title: English
  - 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 Earhart ha volato con il suo monomotore Lockheed Vega 5B attraverso
      l'Atlantico fino a Parigi.
    example_title: Italian
  - 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 Earhart voou em seu monomotor Lockheed Vega 5B através do Atlântico
      para Paris.
    example_title: Portuguese
  - text: >-
      Амелия Эртхарт перелетела на своем одномоторном самолете Lockheed Vega 5B
      через Атлантический океан в Париж.
    example_title: Russian
  - 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
  - text: >-
      Amelia Earhartová přeletěla se svým jednomotorovým Lockheed Vega 5B přes
      Atlantik do Paříže.
    example_title: Czech
  - text: >-
      Amelia Earhart lensi yksimoottorisella Lockheed Vega 5B:llä Atlantin yli
      Pariisiin.
    example_title: Finnish
  - text: >-
      Amelia Earhart fløj med sin enmotoriske Lockheed Vega 5B over Atlanten til
      Paris.
    example_title: Danish
  - text: >-
      Amelia Earhart flög sin enmotoriga Lockheed Vega 5B över Atlanten till
      Paris.
    example_title: Swedish
  - text: >-
      Amelia Earhart fløy sin enmotoriske Lockheed Vega 5B over Atlanterhavet
      til Paris.
    example_title: Norwegian
  - text: >-
      Amelia Earhart și-a zburat cu un singur motor Lockheed Vega 5B peste
      Atlantic până la Paris.
    example_title: Romanian
  - text: >-
      Amelia Earhart menerbangkan mesin tunggal Lockheed Vega 5B melintasi
      Atlantik ke Paris.
    example_title: Indonesian
  - text: >-
      Амелія Эрхарт пераляцела на сваім аднаматорным Lockheed Vega 5B праз
      Атлантыку ў Парыж.
    example_title: Belarusian
  - text: >-
      Амелія Ергарт перелетіла на своєму одномоторному літаку Lockheed Vega 5B
      через Атлантику до Парижа.
    example_title: Ukrainian
  - text: >-
      Amelia Earhart preletjela je svojim jednomotornim zrakoplovom Lockheed
      Vega 5B preko Atlantika do Pariza.
    example_title: Croatian
  - text: >-
      Amelia Earhart lendas oma ühemootoriga Lockheed Vega 5B üle Atlandi
      ookeani Pariisi .
    example_title: Estonian
language:
  - de
  - en
  - es
  - fr
  - it
  - nl
  - pl
  - pt
  - ru
  - zh

span-marker-bert-base-multilingual-cased-multinerd

This model is a fine-tuned version of bert-base-multilingual-cased on an Babelscape/multinerd dataset.

Is your data not (always) capitalized correctly? Then consider using the uncased variant of this model instead for better performance: lxyuan/span-marker-bert-base-multilingual-uncased-multinerd.

This model achieves the following results on the evaluation set:

  • Loss: 0.0049
  • Overall Precision: 0.9242
  • Overall Recall: 0.9281
  • Overall F1: 0.9261
  • Overall Accuracy: 0.9852

Test set results:

  • test_loss: 0.005226554349064827,
  • test_overall_accuracy: 0.9851129807294873,
  • test_overall_f1: 0.9270450073152169,
  • test_overall_precision: 0.9281906912835416,
  • test_overall_recall: 0.9259021481405626,
  • test_runtime: 2690.9722,
  • test_samples_per_second: 150.748,
  • test_steps_per_second: 4.711

This is a replication of Tom's work. Everything remains unchanged, except that we extended the number of training epochs to 3 for a slightly longer training duration and set the gradient_accumulation_steps to 2. Please refer to the official model page to review their results and training script

Results:

Language Precision Recall F1
all 92.42 92.81 92.61
de 95.03 95.07 95.05
en 95.00 95.40 95.20
es 92.05 91.37 91.71
fr 92.37 91.41 91.89
it 91.45 93.15 92.29
nl 93.85 92.98 93.41
pl 93.13 92.66 92.89
pt 93.60 92.50 93.05
ru 93.25 93.32 93.29
zh 89.47 88.40 88.93
  • Special thanks to Tom for creating the evaluation script and generating the results.

Label set

Class Description Examples
PER (person) People Ray Charles, Jessica Alba, Leonardo DiCaprio, Roger Federer, Anna Massey.
ORG (organization) Associations, companies, agencies, institutions, nationalities and religious or political groups University of Edinburgh, San Francisco Giants, Google, Democratic Party.
LOC (location) Physical locations (e.g. mountains, bodies of water), geopolitical entities (e.g. cities, states), and facilities (e.g. bridges, buildings, airports). Rome, Lake Paiku, Chrysler Building, Mount Rushmore, Mississippi River.
ANIM (animal) Breeds of dogs, cats and other animals, including their scientific names. Maine Coon, African Wild Dog, Great White Shark, New Zealand Bellbird.
BIO (biological) Genus of fungus, bacteria and protoctists, families of viruses, and other biological entities. Herpes Simplex Virus, Escherichia Coli, Salmonella, Bacillus Anthracis.
CEL (celestial) Planets, stars, asteroids, comets, nebulae, galaxies and other astronomical objects. Sun, Neptune, Asteroid 187 Lamberta, Proxima Centauri, V838 Monocerotis.
DIS (disease) Physical, mental, infectious, non-infectious, deficiency, inherited, degenerative, social and self-inflicted diseases. Alzheimer’s Disease, Cystic Fibrosis, Dilated Cardiomyopathy, Arthritis.
EVE (event) Sport events, battles, wars and other events. American Civil War, 2003 Wimbledon Championships, Cannes Film Festival.
FOOD (food) Foods and drinks. Carbonara, Sangiovese, Cheddar Beer Fondue, Pizza Margherita.
INST (instrument) Technological instruments, mechanical instruments, musical instruments, and other tools. Spitzer Space Telescope, Commodore 64, Skype, Apple Watch, Fender Stratocaster.
MEDIA (media) Titles of films, books, magazines, songs and albums, fictional characters and languages. Forbes, American Psycho, Kiss Me Once, Twin Peaks, Disney Adventures.
PLANT (plant) Types of trees, flowers, and other plants, including their scientific names. Salix, Quercus Petraea, Douglas Fir, Forsythia, Artemisia Maritima.
MYTH (mythological) Mythological and religious entities. Apollo, Persephone, Aphrodite, Saint Peter, Pope Gregory I, Hercules.
TIME (time) Specific and well-defined time intervals, such as eras, historical periods, centuries, years and important days. No months and days of the week. Renaissance, Middle Ages, Christmas, Great Depression, 17th Century, 2012.
VEHI (vehicle) Cars, motorcycles and other vehicles. Ferrari Testarossa, Suzuki Jimny, Honda CR-X, Boeing 747, Fairey Fulmar.

Inference Example 

# install span_marker
(env)$ pip install span_marker


from span_marker import SpanMarkerModel

model = SpanMarkerModel.from_pretrained("lxyuan/span-marker-bert-base-multilingual-cased-multinerd")

description = "Singapore is renowned for its hawker centers offering dishes \
like Hainanese chicken rice and laksa, while Malaysia boasts dishes such as \
nasi lemak and rendang, reflecting its rich culinary heritage."

entities = model.predict(description)

entities
>>>
[
  {'span': 'Singapore', 'label': 'LOC', 'score': 0.999988317489624, 'char_start_index': 0, 'char_end_index': 9},
  {'span': 'Hainanese chicken rice', 'label': 'FOOD', 'score': 0.9894770383834839, 'char_start_index': 66, 'char_end_index': 88},
  {'span': 'laksa', 'label': 'FOOD', 'score': 0.9224908947944641, 'char_start_index': 93, 'char_end_index': 98},
  {'span': 'Malaysia', 'label': 'LOC', 'score': 0.9999839067459106, 'char_start_index': 106, 'char_end_index': 114}]

# missed: nasi lemak as FOOD
# missed: rendang as FOOD
# :(

Quick test on Chinese 

from span_marker import SpanMarkerModel

model = SpanMarkerModel.from_pretrained("lxyuan/span-marker-bert-base-multilingual-cased-multinerd")

# translate to chinese
description = "Singapore is renowned for its hawker centers offering dishes \
like Hainanese chicken rice and laksa, while Malaysia boasts dishes such as \
nasi lemak and rendang, reflecting its rich culinary heritage."

zh_description = "新加坡因其小贩中心提供海南鸡饭和叻沙等菜肴而闻名, 而马来西亚则拥有椰浆饭和仁当等菜肴，反映了其丰富的烹饪传统."

entities = model.predict(zh_description)

entities
>>>
[
  {'span': '新加坡', 'label': 'LOC', 'score': 0.9282007813453674, 'char_start_index': 0, 'char_end_index': 3},
  {'span': '马来西亚', 'label': 'LOC', 'score': 0.7439665794372559, 'char_start_index': 27, 'char_end_index': 31}]

# It only managed to capture two countries: Singapore and Malaysia.
# All other entities were missed out.

Training procedure

One can reproduce the result running this script

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • 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

Training Loss Epoch Step Validation Loss Overall Precision Overall Recall Overall F1 Overall Accuracy
0.0129 1.0 50436 0.0042 0.9226 0.9169 0.9197 0.9837
0.0027 2.0 100873 0.0043 0.9255 0.9206 0.9230 0.9846
0.0015 3.0 151308 0.0049 0.9242 0.9281 0.9261 0.9852

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.3
  • Tokenizers 0.13.3