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span-marker-bert-base-uncased-cross-ner

Token Classification
SpanMarker
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named-entity-recognition
Eval Results
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metadata
license: apache-2.0
library_name: span-marker
tags:
  - span-marker
  - token-classification
  - ner
  - named-entity-recognition
pipeline_tag: token-classification
widget:
  - text: >-
      the model is initially fit on a training dataset, the model (e.g. a neural
      net or a naive bayes classifier) is trained on the training dataset using
      a supervised learning method, for example using optimization methods such
      as gradient descent or stochastic gradient descent.
    example_title: AI
  - text: >-
      it restricted the barbarians' selectorial options but they still boast 13
      internationals including england full-back tim stimpson and recalled wing
      tony underwood, plus all black forwards ian jones and norm hewitt.
    example_title: CoNLL
  - text: >-
      two decades after frank herbert's death, his son brian herbert, along with
      kevin j. anderson, published two sequels - hunters of dune (2006) and
      sandworms of dune (2007) - based on notes left behind by frank herbert for
      what he referred to as dune 7, his own planned seventh novel in the dune
      series.
    example_title: Literature
  - text: >-
      polka is still a popular genre of folk music in many european countries
      and is performed by folk artists in poland, latvia, lithuania, czech
      republic, netherlands, croatia, slovenia, germany, hungary, austria,
      switzerland, italy, ukraine, belarus, russia and slovakia.
    example_title: Music 1
  - text: >-
      as a strong advocate of animal rights, linda lent her support to many
      organizations such as people for the ethical treatment of animals (peta),
      the campaign to protect rural england, and friends of the earth.
    example_title: Music 2
  - text: >-
      some of the most pronounced effects of hellenization can be seen in
      afghanistan and india, in the region of the relatively late-rising
      greco-bactrian kingdom (250-125 bc) (in modern afghanistan, pakistan, and
      tajikistan) and the indo-greek kingdom (180 bc - 10 ad) in modern
      afghanistan and india and created a culture of greco-buddhist art.
    example_title: Politics
  - text: >-
      that first evening session was organized by jack yardley from johns
      hopkins university, and included henry appelman (university of michigan) ,
      harvey goldman (beth israel deaconess medical center and harvard medical
      school), bill hawk (the cleveland clinic), tom kent (university of iowa),
      si-chun ming (temple university), tom norris (university of washington),
      and robert riddell (university of chicago).
    example_title: Science 1
  - text: >-
      viral tk phosphorylates aciclovir into its monophosphate form, which is
      subsequently phosphorylated to active aciclovir triphoshate by cellular
      kinases, thus selectively inhibiting viral dna polymerase.
    example_title: Science 2
model-index:
  - name: SpanMarker w. bert-base-uncased on CrossNER by Tom Aarsen
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          type: P3ps/Cross_ner
          name: CrossNER
          split: test
          revision: 7cecbbb3d2eb8c75c8571c53e5a5270cfd0c5a9e
        metrics:
          - type: f1
            value: 0.8708
            name: F1
          - type: precision
            value: 0.8763
            name: Precision
          - type: recall
            value: 0.8654
            name: Recall
datasets:
  - P3ps/Cross_ner
language:
  - en
metrics:
  - f1
  - recall
  - precision

SpanMarker for uncased Named Entity Recognition

This is a SpanMarker model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses bert-base-uncased as the underlying encoder. See train.py for the training script. It is trained on P3ps/Cross_ner, which I believe is a variant of DFKI-SLT/cross_ner that merged the validation set into the training set and applied deduplication.

Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance: tomaarsen/span-marker-bert-base-cross-ner.

Labels & Metrics

Label Examples Precision Recall F1
all - 87.63 86.54 87.08
academicjournal "new journal of physics", "epl", "european physical journal b" 82.22 90.24 86.05
album "tellin' stories", "generation terrorists", "country airs" 84.46 84.46 84.46
algorithm "lda", "pca", "gradient descent" 82.86 76.99 79.82
astronomicalobject "earth", "sun", "halley's comet" 88.61 94.59 91.50
award "nobel prize for literature", "acamedy award for best actress", "mandelbrot's awards" 87.76 91.63 89.66
band "clash", "parliament funkadelic", "sly and the family stone" 82.72 85.35 84.01
book "nietzsche contra wagner" , "dionysian-dithyrambs", "the rebel" 68.51 79.49 73.59
chemicalcompound "hydrogen sulfide", "starch", "lactic acid" 73.33 66.67 69.84
chemicalelement "potassium", "fluorine", "chlorine" 95.65 73.33 83.02
conference "siggraph", "ijcai", "ieee transactions on speech and audio processing" 72.41 60.00 65.62
country "united arab emirates", "u.s.", "canada" 81.03 86.08 83.48
discipline "physics", "meteorology", "geography" 35.48 40.74 37.93
election "2004 canadian federal election", "2006 canadian federal election", "1999 scottish parliament election" 96.22 98.28 97.24
enzyme "rna polymerase", "phosphoinositide 3-kinase", "protein kinase c" 72.09 83.78 77.50
event "cannes film festival", "2019 special olympics world summer games", "2017 western iraq campaign" 68.12 60.22 63.93
field "computational imaging", "electronics", "information theory" 92.13 77.36 84.10
literarygenre "novel", "satire", "short story" 65.26 72.09 68.51
location "china", "bombay", "serbia" 94.78 93.68 94.23
magazine "the atlantic", "the american spectator", "astounding science fiction" 60.71 60.71 60.71
metrics "bleu", "precision", "dcg" 77.01 82.72 79.76
misc "serbian", "belgian", "the birth of a nation" 80.11 72.12 75.91
musicalartist "chuck burgi", "john miceli", "john o'reilly" 78.84 84.44 81.55
musicalinstrument "koto", "bubens", "def" 75.00 33.33 46.15
musicgenre "christian rock", "punk rock", "romantic melodicism" 88.21 88.21 88.21
organisation "irish times", "comintern", "wimbledon" 89.17 89.98 89.57
person "gong zhichao", "liu lufung", "margret crowley" 95.87 92.65 94.23
poem "historia destructionis troiae", "i am joaquin", "the snow man" 94.29 64.71 76.74
politicalparty "new democratic party", "bloc québécois", "liberal party of canada" 87.16 84.50 85.81
politician "susan kadis", "simon strelchik", "lloyd helferty" 85.23 90.71 87.89
product "alphago", "wordnet", "facial recognition system" 63.95 65.48 64.71
programlang "r", "c++", "java" 75.00 84.38 79.41
protein "dna methyltransferase", "tau protein", "amyloid beta" 57.50 66.67 61.74
researcher "sirovich", "kirby", "matthew turk" 93.06 75.28 83.23
scientist "matjaž perc", "cotton", "singer" 80.27 93.72 86.47
song "right where i'm supposed to be", "easy", "three times a lady" 89.87 82.56 86.06
task "robot control", "elevator scheduling", "telecommunications" 73.86 75.58 74.71
theory "big bang", "general theory of relativity", "ptolemaic planetary theories" 0.00 0.00 0.00
university "university of göttingen", "duke", "imperial academy of sciences" 79.78 79.78 79.78
writer "thomas mann", "george bernard shaw", "thomas hardy" 77.78 86.19 81.77

Usage

To use this model for inference, first install the span_marker library:

pip install span_marker

You can then run inference with this model like so:

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-cross-ner")
# Run inference
entities = model.predict("amelia earhart flew her single engine lockheed vega 5b across the atlantic to paris.")

See the SpanMarker repository for documentation and additional information on this library.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • 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 results

Training Loss Epoch Step Validation Loss Overall Precision Overall Recall Overall F1 Overall Accuracy
0.0641 0.25 200 0.0445 0.7141 0.5496 0.6212 0.8700
0.0268 0.5 400 0.0224 0.8171 0.7510 0.7827 0.9314
0.0213 0.76 600 0.0187 0.8387 0.8013 0.8196 0.9444
0.017 1.01 800 0.0162 0.8623 0.8231 0.8422 0.9497
0.0141 1.26 1000 0.0163 0.8571 0.8384 0.8477 0.9535
0.0132 1.51 1200 0.0149 0.8711 0.8470 0.8589 0.9563
0.0113 1.76 1400 0.0150 0.8603 0.8523 0.8563 0.9556
0.0097 2.02 1600 0.0150 0.8710 0.8553 0.8631 0.9573
0.0083 2.27 1800 0.0148 0.8809 0.8568 0.8687 0.9586
0.0075 2.52 2000 0.0150 0.8733 0.8573 0.8652 0.9583
0.0068 2.77 2200 0.0148 0.8745 0.8642 0.8693 0.9600

Framework versions

  • SpanMarker 1.2.4
  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.3
  • Tokenizers 0.13.2