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--- |
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license: cc-by-nc-sa-4.0 |
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library_name: span-marker |
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tags: |
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- span-marker |
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- token-classification |
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- ner |
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- named-entity-recognition |
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pipeline_tag: token-classification |
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widget: |
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- text: "Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B über den Atlantik nach Paris." |
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example_title: "German" |
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- text: "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris." |
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example_title: "English" |
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- text: "Amelia Earthart voló su Lockheed Vega 5B monomotor a través del Océano Atlántico hasta París." |
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example_title: "Spanish" |
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- text: "Amelia Earthart a fait voler son monomoteur Lockheed Vega 5B à travers l'ocean Atlantique jusqu'à Paris." |
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example_title: "French" |
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- text: "Amelia Earhart ha volato con il suo monomotore Lockheed Vega 5B attraverso l'Atlantico fino a Parigi." |
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example_title: "Italian" |
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- text: "Amelia Earthart vloog met haar één-motorige Lockheed Vega 5B over de Atlantische Oceaan naar Parijs." |
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example_title: "Dutch" |
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- text: "Amelia Earthart przeleciała swoim jednosilnikowym samolotem Lockheed Vega 5B przez Ocean Atlantycki do Paryża." |
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example_title: "Polish" |
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- text: "Amelia Earhart voou em seu monomotor Lockheed Vega 5B através do Atlântico para Paris." |
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example_title: "Portuguese" |
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- text: "Амелия Эртхарт перелетела на своем одномоторном самолете Lockheed Vega 5B через Атлантический океан в Париж." |
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example_title: "Russian" |
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- text: "Amelia Earthart flaug eins hreyfils Lockheed Vega 5B yfir Atlantshafið til Parísar." |
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example_title: "Icelandic" |
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- text: "Η Amelia Earthart πέταξε το μονοκινητήριο Lockheed Vega 5B της πέρα από τον Ατλαντικό Ωκεανό στο Παρίσι." |
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example_title: "Greek" |
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- text: "Amelia Earhartová přeletěla se svým jednomotorovým Lockheed Vega 5B přes Atlantik do Paříže." |
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example_title: "Czech" |
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- text: "Amelia Earhart lensi yksimoottorisella Lockheed Vega 5B:llä Atlantin yli Pariisiin." |
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example_title: "Finnish" |
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- text: "Amelia Earhart fløj med sin enmotoriske Lockheed Vega 5B over Atlanten til Paris." |
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example_title: "Danish" |
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- text: "Amelia Earhart flög sin enmotoriga Lockheed Vega 5B över Atlanten till Paris." |
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example_title: "Swedish" |
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- text: "Amelia Earhart fløy sin enmotoriske Lockheed Vega 5B over Atlanterhavet til Paris." |
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example_title: "Norwegian" |
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- text: "Amelia Earhart și-a zburat cu un singur motor Lockheed Vega 5B peste Atlantic până la Paris." |
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example_title: "Romanian" |
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- text: "Amelia Earhart menerbangkan mesin tunggal Lockheed Vega 5B melintasi Atlantik ke Paris." |
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example_title: "Indonesian" |
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- text: "Амелія Эрхарт пераляцела на сваім аднаматорным Lockheed Vega 5B праз Атлантыку ў Парыж." |
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example_title: "Belarusian" |
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- text: "Амелія Ергарт перелетіла на своєму одномоторному літаку Lockheed Vega 5B через Атлантику до Парижа." |
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example_title: "Ukrainian" |
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- text: "Amelia Earhart preletjela je svojim jednomotornim zrakoplovom Lockheed Vega 5B preko Atlantika do Pariza." |
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example_title: "Croatian" |
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- text: "Amelia Earhart lendas oma ühemootoriga Lockheed Vega 5B üle Atlandi ookeani Pariisi ." |
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example_title: "Estonian" |
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model-index: |
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- name: SpanMarker w. bert-base-multilingual-cased on MultiNERD by Tom Aarsen |
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results: |
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- task: |
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type: token-classification |
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name: Named Entity Recognition |
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dataset: |
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type: Babelscape/multinerd |
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name: MultiNERD |
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split: test |
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revision: 2814b78e7af4b5a1f1886fe7ad49632de4d9dd25 |
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metrics: |
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- type: f1 |
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value: 0.92478 |
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name: F1 |
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- type: precision |
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value: 0.93385 |
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name: Precision |
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- type: recall |
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value: 0.91588 |
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name: Recall |
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datasets: |
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- Babelscape/multinerd |
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language: |
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- multilingual |
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metrics: |
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- f1 |
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- recall |
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- precision |
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base_model: bert-base-multilingual-cased |
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--- |
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# SpanMarker for Multilingual Named Entity Recognition |
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for multilingual Named Entity Recognition trained on the [MultiNERD](https://huggingface.co/datasets/Babelscape/multinerd) dataset. In particular, this SpanMarker model uses [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) as the underlying encoder. See [train.py](train.py) for the training script. |
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Is your data not (always) capitalized correctly? Then consider using this uncased variant of this model by [@lxyuan](https://huggingface.co/lxyuan) for better performance: |
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[lxyuan/span-marker-bert-base-multilingual-uncased-multinerd](https://huggingface.co/lxyuan/span-marker-bert-base-multilingual-uncased-multinerd). |
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## Metrics |
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| **Language** | **Precision** | **Recall** | **F1** | |
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|--------------|---------------|------------|------------| |
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| **all** | 93.39 | 91.59 | **92.48** | |
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| **de** | 95.21 | 94.32 | **94.76** | |
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| **en** | 95.07 | 95.29 | **95.18** | |
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| **es** | 93.50 | 89.65 | **91.53** | |
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| **fr** | 93.86 | 90.07 | **91.92** | |
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| **it** | 91.63 | 93.57 | **92.59** | |
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| **nl** | 94.86 | 91.74 | **93.27** | |
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| **pl** | 93.51 | 91.83 | **92.66** | |
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| **pt** | 94.48 | 91.30 | **92.86** | |
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| **ru** | 93.70 | 93.10 | **93.39** | |
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| **zh** | 88.36 | 85.71 | **87.02** | |
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## Label set |
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| Class | Description | Examples | |
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|-------|-------------|----------| |
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PER (person) | People | Ray Charles, Jessica Alba, Leonardo DiCaprio, Roger Federer, Anna Massey. | |
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ORG (organization) | Associations, companies, agencies, institutions, nationalities and religious or political groups | University of Edinburgh, San Francisco Giants, Google, Democratic Party. | |
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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. | |
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ANIM (animal) | Breeds of dogs, cats and other animals, including their scientific names. | Maine Coon, African Wild Dog, Great White Shark, New Zealand Bellbird. | |
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BIO (biological) | Genus of fungus, bacteria and protoctists, families of viruses, and other biological entities. | Herpes Simplex Virus, Escherichia Coli, Salmonella, Bacillus Anthracis. | |
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CEL (celestial) | Planets, stars, asteroids, comets, nebulae, galaxies and other astronomical objects. | Sun, Neptune, Asteroid 187 Lamberta, Proxima Centauri, V838 Monocerotis. | |
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DIS (disease) | Physical, mental, infectious, non-infectious, deficiency, inherited, degenerative, social and self-inflicted diseases. | Alzheimer’s Disease, Cystic Fibrosis, Dilated Cardiomyopathy, Arthritis. | |
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EVE (event) | Sport events, battles, wars and other events. | American Civil War, 2003 Wimbledon Championships, Cannes Film Festival. | |
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FOOD (food) | Foods and drinks. | Carbonara, Sangiovese, Cheddar Beer Fondue, Pizza Margherita. | |
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INST (instrument) | Technological instruments, mechanical instruments, musical instruments, and other tools. | Spitzer Space Telescope, Commodore 64, Skype, Apple Watch, Fender Stratocaster. | |
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MEDIA (media) | Titles of films, books, magazines, songs and albums, fictional characters and languages. | Forbes, American Psycho, Kiss Me Once, Twin Peaks, Disney Adventures. | |
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PLANT (plant) | Types of trees, flowers, and other plants, including their scientific names. | Salix, Quercus Petraea, Douglas Fir, Forsythia, Artemisia Maritima. | |
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MYTH (mythological) | Mythological and religious entities. | Apollo, Persephone, Aphrodite, Saint Peter, Pope Gregory I, Hercules. | |
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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. | |
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VEHI (vehicle) | Cars, motorcycles and other vehicles. | Ferrari Testarossa, Suzuki Jimny, Honda CR-X, Boeing 747, Fairey Fulmar. |
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## Usage |
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To use this model for inference, first install the `span_marker` library: |
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```bash |
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pip install span_marker |
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``` |
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You can then run inference with this model like so: |
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```python |
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from span_marker import SpanMarkerModel |
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# Download from the 🤗 Hub |
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model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-multinerd") |
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# Run inference |
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entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") |
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``` |
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See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:------:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.0179 | 0.01 | 1000 | 0.0146 | 0.8101 | 0.7616 | 0.7851 | 0.9530 | |
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| 0.0099 | 0.02 | 2000 | 0.0091 | 0.8571 | 0.8425 | 0.8498 | 0.9663 | |
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| 0.0085 | 0.03 | 3000 | 0.0078 | 0.8729 | 0.8579 | 0.8653 | 0.9700 | |
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| 0.0075 | 0.04 | 4000 | 0.0072 | 0.8821 | 0.8724 | 0.8772 | 0.9739 | |
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| 0.0074 | 0.05 | 5000 | 0.0075 | 0.8622 | 0.8841 | 0.8730 | 0.9722 | |
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| 0.0074 | 0.06 | 6000 | 0.0067 | 0.9056 | 0.8568 | 0.8805 | 0.9749 | |
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| 0.0066 | 0.07 | 7000 | 0.0065 | 0.9082 | 0.8543 | 0.8804 | 0.9737 | |
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| 0.0063 | 0.08 | 8000 | 0.0066 | 0.9039 | 0.8617 | 0.8823 | 0.9745 | |
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| 0.0062 | 0.09 | 9000 | 0.0062 | 0.9323 | 0.8425 | 0.8852 | 0.9754 | |
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| 0.007 | 0.1 | 10000 | 0.0066 | 0.8898 | 0.8758 | 0.8827 | 0.9746 | |
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| 0.006 | 0.11 | 11000 | 0.0061 | 0.8986 | 0.8841 | 0.8913 | 0.9766 | |
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| 0.006 | 0.12 | 12000 | 0.0061 | 0.9171 | 0.8628 | 0.8891 | 0.9763 | |
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| 0.0062 | 0.13 | 13000 | 0.0060 | 0.9264 | 0.8634 | 0.8938 | 0.9772 | |
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| 0.0059 | 0.14 | 14000 | 0.0059 | 0.9323 | 0.8508 | 0.8897 | 0.9763 | |
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| 0.0059 | 0.15 | 15000 | 0.0060 | 0.9011 | 0.8815 | 0.8912 | 0.9758 | |
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| 0.0059 | 0.16 | 16000 | 0.0060 | 0.9221 | 0.8598 | 0.8898 | 0.9763 | |
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| 0.0056 | 0.17 | 17000 | 0.0058 | 0.9098 | 0.8839 | 0.8967 | 0.9775 | |
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| 0.0055 | 0.18 | 18000 | 0.0060 | 0.9103 | 0.8739 | 0.8917 | 0.9765 | |
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| 0.0054 | 0.19 | 19000 | 0.0056 | 0.9135 | 0.8726 | 0.8925 | 0.9774 | |
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| 0.0052 | 0.2 | 20000 | 0.0058 | 0.9108 | 0.8834 | 0.8969 | 0.9773 | |
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| 0.0053 | 0.21 | 21000 | 0.0058 | 0.9038 | 0.8866 | 0.8951 | 0.9773 | |
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| 0.0057 | 0.22 | 22000 | 0.0057 | 0.9130 | 0.8762 | 0.8942 | 0.9775 | |
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| 0.0056 | 0.23 | 23000 | 0.0053 | 0.9375 | 0.8604 | 0.8973 | 0.9781 | |
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| 0.005 | 0.24 | 24000 | 0.0054 | 0.9253 | 0.8822 | 0.9032 | 0.9784 | |
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| 0.0055 | 0.25 | 25000 | 0.0055 | 0.9182 | 0.8807 | 0.8991 | 0.9787 | |
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| 0.0049 | 0.26 | 26000 | 0.0053 | 0.9311 | 0.8702 | 0.8997 | 0.9783 | |
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| 0.0051 | 0.27 | 27000 | 0.0054 | 0.9192 | 0.8877 | 0.9032 | 0.9787 | |
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| 0.0051 | 0.28 | 28000 | 0.0053 | 0.9332 | 0.8783 | 0.9049 | 0.9795 | |
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| 0.0049 | 0.29 | 29000 | 0.0054 | 0.9311 | 0.8672 | 0.8981 | 0.9789 | |
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| 0.0047 | 0.3 | 30000 | 0.0054 | 0.9165 | 0.8954 | 0.9058 | 0.9796 | |
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| 0.005 | 0.31 | 31000 | 0.0052 | 0.9079 | 0.9016 | 0.9047 | 0.9787 | |
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| 0.0051 | 0.32 | 32000 | 0.0051 | 0.9157 | 0.9001 | 0.9078 | 0.9796 | |
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| 0.0046 | 0.33 | 33000 | 0.0051 | 0.9147 | 0.8935 | 0.9040 | 0.9788 | |
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| 0.0046 | 0.34 | 34000 | 0.0050 | 0.9229 | 0.8847 | 0.9034 | 0.9793 | |
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| 0.005 | 0.35 | 35000 | 0.0051 | 0.9198 | 0.8922 | 0.9058 | 0.9796 | |
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| 0.0047 | 0.36 | 36000 | 0.0050 | 0.9321 | 0.8890 | 0.9100 | 0.9807 | |
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| 0.0048 | 0.37 | 37000 | 0.0050 | 0.9046 | 0.9133 | 0.9089 | 0.9800 | |
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| 0.0046 | 0.38 | 38000 | 0.0051 | 0.9170 | 0.8973 | 0.9071 | 0.9806 | |
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| 0.0048 | 0.39 | 39000 | 0.0050 | 0.9417 | 0.8775 | 0.9084 | 0.9805 | |
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| 0.0042 | 0.4 | 40000 | 0.0049 | 0.9238 | 0.8937 | 0.9085 | 0.9797 | |
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| 0.0038 | 0.41 | 41000 | 0.0048 | 0.9371 | 0.8920 | 0.9140 | 0.9812 | |
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| 0.0042 | 0.42 | 42000 | 0.0048 | 0.9359 | 0.8862 | 0.9104 | 0.9808 | |
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| 0.0051 | 0.43 | 43000 | 0.0049 | 0.9080 | 0.9060 | 0.9070 | 0.9805 | |
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| 0.0037 | 0.44 | 44000 | 0.0049 | 0.9328 | 0.8877 | 0.9097 | 0.9801 | |
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| 0.0041 | 0.45 | 45000 | 0.0049 | 0.9231 | 0.8975 | 0.9101 | 0.9813 | |
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| 0.0046 | 0.46 | 46000 | 0.0046 | 0.9308 | 0.8943 | 0.9122 | 0.9812 | |
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| 0.0038 | 0.47 | 47000 | 0.0047 | 0.9291 | 0.8969 | 0.9127 | 0.9815 | |
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| 0.0043 | 0.48 | 48000 | 0.0046 | 0.9308 | 0.8909 | 0.9104 | 0.9804 | |
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| 0.0043 | 0.49 | 49000 | 0.0046 | 0.9278 | 0.8954 | 0.9113 | 0.9800 | |
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| 0.0039 | 0.5 | 50000 | 0.0047 | 0.9173 | 0.9073 | 0.9123 | 0.9817 | |
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| 0.0043 | 0.51 | 51000 | 0.0045 | 0.9347 | 0.8962 | 0.9150 | 0.9821 | |
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| 0.0047 | 0.52 | 52000 | 0.0045 | 0.9266 | 0.9016 | 0.9139 | 0.9810 | |
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| 0.0035 | 0.53 | 53000 | 0.0046 | 0.9165 | 0.9122 | 0.9144 | 0.9820 | |
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| 0.0038 | 0.54 | 54000 | 0.0046 | 0.9231 | 0.9050 | 0.9139 | 0.9823 | |
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| 0.0036 | 0.55 | 55000 | 0.0046 | 0.9331 | 0.9005 | 0.9165 | 0.9828 | |
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| 0.0037 | 0.56 | 56000 | 0.0047 | 0.9246 | 0.9016 | 0.9129 | 0.9821 | |
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| 0.0035 | 0.57 | 57000 | 0.0044 | 0.9351 | 0.9003 | 0.9174 | 0.9829 | |
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| 0.0043 | 0.57 | 58000 | 0.0043 | 0.9257 | 0.9079 | 0.9167 | 0.9826 | |
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| 0.004 | 0.58 | 59000 | 0.0043 | 0.9286 | 0.9065 | 0.9174 | 0.9823 | |
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| 0.0041 | 0.59 | 60000 | 0.0044 | 0.9324 | 0.9050 | 0.9185 | 0.9825 | |
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| 0.0039 | 0.6 | 61000 | 0.0044 | 0.9268 | 0.9041 | 0.9153 | 0.9815 | |
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| 0.0038 | 0.61 | 62000 | 0.0043 | 0.9367 | 0.8918 | 0.9137 | 0.9819 | |
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| 0.0037 | 0.62 | 63000 | 0.0044 | 0.9249 | 0.9160 | 0.9205 | 0.9833 | |
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| 0.0036 | 0.63 | 64000 | 0.0043 | 0.9398 | 0.8975 | 0.9181 | 0.9827 | |
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| 0.0036 | 0.64 | 65000 | 0.0043 | 0.9260 | 0.9118 | 0.9188 | 0.9829 | |
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| 0.0035 | 0.65 | 66000 | 0.0044 | 0.9375 | 0.8988 | 0.9178 | 0.9828 | |
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| 0.0034 | 0.66 | 67000 | 0.0043 | 0.9272 | 0.9143 | 0.9207 | 0.9833 | |
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| 0.0033 | 0.67 | 68000 | 0.0044 | 0.9332 | 0.9024 | 0.9176 | 0.9827 | |
|
| 0.0035 | 0.68 | 69000 | 0.0044 | 0.9396 | 0.8981 | 0.9184 | 0.9825 | |
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| 0.0038 | 0.69 | 70000 | 0.0042 | 0.9265 | 0.9163 | 0.9214 | 0.9827 | |
|
| 0.0035 | 0.7 | 71000 | 0.0044 | 0.9375 | 0.9013 | 0.9191 | 0.9827 | |
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| 0.0037 | 0.71 | 72000 | 0.0042 | 0.9264 | 0.9171 | 0.9217 | 0.9830 | |
|
| 0.0039 | 0.72 | 73000 | 0.0043 | 0.9399 | 0.9003 | 0.9197 | 0.9826 | |
|
| 0.0039 | 0.73 | 74000 | 0.0041 | 0.9341 | 0.9094 | 0.9216 | 0.9832 | |
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| 0.0035 | 0.74 | 75000 | 0.0042 | 0.9301 | 0.9160 | 0.9230 | 0.9837 | |
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| 0.0037 | 0.75 | 76000 | 0.0042 | 0.9342 | 0.9107 | 0.9223 | 0.9835 | |
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| 0.0034 | 0.76 | 77000 | 0.0042 | 0.9331 | 0.9118 | 0.9223 | 0.9836 | |
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| 0.003 | 0.77 | 78000 | 0.0041 | 0.9330 | 0.9135 | 0.9231 | 0.9838 | |
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| 0.0034 | 0.78 | 79000 | 0.0041 | 0.9308 | 0.9082 | 0.9193 | 0.9832 | |
|
| 0.0037 | 0.79 | 80000 | 0.0040 | 0.9346 | 0.9128 | 0.9236 | 0.9839 | |
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| 0.0032 | 0.8 | 81000 | 0.0041 | 0.9389 | 0.9128 | 0.9257 | 0.9841 | |
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| 0.0031 | 0.81 | 82000 | 0.0040 | 0.9293 | 0.9163 | 0.9227 | 0.9836 | |
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| 0.0032 | 0.82 | 83000 | 0.0041 | 0.9305 | 0.9160 | 0.9232 | 0.9835 | |
|
| 0.0034 | 0.83 | 84000 | 0.0041 | 0.9327 | 0.9118 | 0.9221 | 0.9838 | |
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| 0.0028 | 0.84 | 85000 | 0.0041 | 0.9279 | 0.9216 | 0.9247 | 0.9839 | |
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| 0.0031 | 0.85 | 86000 | 0.0041 | 0.9326 | 0.9167 | 0.9246 | 0.9838 | |
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| 0.0029 | 0.86 | 87000 | 0.0040 | 0.9354 | 0.9158 | 0.9255 | 0.9841 | |
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| 0.0031 | 0.87 | 88000 | 0.0041 | 0.9327 | 0.9156 | 0.9241 | 0.9840 | |
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| 0.0033 | 0.88 | 89000 | 0.0040 | 0.9367 | 0.9141 | 0.9253 | 0.9846 | |
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| 0.0031 | 0.89 | 90000 | 0.0040 | 0.9379 | 0.9141 | 0.9259 | 0.9844 | |
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| 0.0031 | 0.9 | 91000 | 0.0040 | 0.9297 | 0.9184 | 0.9240 | 0.9843 | |
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| 0.0034 | 0.91 | 92000 | 0.0040 | 0.9299 | 0.9188 | 0.9243 | 0.9843 | |
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| 0.0036 | 0.92 | 93000 | 0.0039 | 0.9324 | 0.9175 | 0.9249 | 0.9843 | |
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| 0.0028 | 0.93 | 94000 | 0.0039 | 0.9399 | 0.9135 | 0.9265 | 0.9848 | |
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| 0.0029 | 0.94 | 95000 | 0.0040 | 0.9342 | 0.9173 | 0.9257 | 0.9845 | |
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| 0.003 | 0.95 | 96000 | 0.0040 | 0.9378 | 0.9184 | 0.9280 | 0.9850 | |
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| 0.0029 | 0.96 | 97000 | 0.0039 | 0.9380 | 0.9152 | 0.9264 | 0.9847 | |
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| 0.003 | 0.97 | 98000 | 0.0039 | 0.9372 | 0.9156 | 0.9263 | 0.9849 | |
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| 0.003 | 0.98 | 99000 | 0.0039 | 0.9387 | 0.9167 | 0.9276 | 0.9851 | |
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| 0.0031 | 0.99 | 100000 | 0.0039 | 0.9373 | 0.9177 | 0.9274 | 0.9849 | |
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### Framework versions |
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- SpanMarker 1.2.4 |
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- Transformers 4.28.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.2 |
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## See also |
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* [lxyuan/span-marker-bert-base-multilingual-cased-multinerd](https://huggingface.co/lxyuan/span-marker-bert-base-multilingual-cased-multinerd) is similar to this model, but trained on 3 epochs instead of 2. It reaches better performance on 7 out of the 10 languages. |
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* [lxyuan/span-marker-bert-base-multilingual-uncased-multinerd](https://huggingface.co/lxyuan/span-marker-bert-base-multilingual-uncased-multinerd) is a strong uncased variant of this model, also trained on 3 epochs instead of 2. |
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## Contributions |
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Many thanks to [Simone Tedeschi](https://huggingface.co/sted97) from [Babelscape](https://babelscape.com) for his insight when training this model and his involvement in the creation of the training dataset. |
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