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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 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 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 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 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: "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 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 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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model-index: |
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- name: SpanMarker w. xlm-roberta-base 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: 0.91314 |
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name: F1 |
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- type: precision |
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value: 0.91994 |
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name: Precision |
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- type: recall |
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value: 0.90643 |
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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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--- |
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# SpanMarker for Named Entity Recognition |
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**Note**: Due to major [tokenization limitations](#Limitations), this model is deprecated in favor of the much superior [tomaarsen/span-marker-mbert-base-multinerd](https://huggingface.co/tomaarsen/span-marker-mbert-base-multinerd) model. |
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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 [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder. See [train.py](train.py) for the training script. |
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## Metrics |
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| **Language** | **Precision** | **Recall** | **F1** | |
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|--------------|---------------|------------|------------| |
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| **all** | 91.99 | 90.64 | **91.31** | |
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| **de** | 93.56 | 93.87 | **93.77** | |
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| **en** | 94.01 | 95.10 | **94.55** | |
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| **es** | 92.58 | 89.13 | **90.82** | |
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| **fr** | 93.23 | 88.68 | **90.90** | |
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| **it** | 90.23 | 92.60 | **93.40** | |
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| **nl** | 93.61 | 91.36 | **92.47** | |
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| **pl** | 92.51 | 90.81 | **91.66** | |
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| **pt** | 93.29 | 90.22 | **91.73** | |
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| **ru** | 92.37 | 92.91 | **92.64** | |
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| **zh** | 83.23 | 81.55 | **82.38** | |
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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-xlm-roberta-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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## 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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