tomaarsen's picture
tomaarsen HF staff
Remove limitation since v1.4.0 release
dec5a5c
---
license: cc-by-nc-sa-4.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
pipeline_tag: token-classification
widget:
- text: "Amelia Earthart voló su Lockheed Vega 5B monomotor a través del Océano Atlántico hasta París ."
example_title: "Spanish"
- text: "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris ."
example_title: "English"
- 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"
model-index:
- name: SpanMarker w. xlm-roberta-base on MultiNERD by Tom Aarsen
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
type: Babelscape/multinerd
name: MultiNERD
split: test
revision: 2814b78e7af4b5a1f1886fe7ad49632de4d9dd25
metrics:
- type: f1
value: 0.91314
name: F1
- type: precision
value: 0.91994
name: Precision
- type: recall
value: 0.90643
name: Recall
datasets:
- Babelscape/multinerd
language:
- multilingual
metrics:
- f1
- recall
- precision
---
# SpanMarker for Named Entity Recognition
**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.
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.
## Metrics
| **Language** | **Precision** | **Recall** | **F1** |
|--------------|---------------|------------|------------|
| **all** | 91.99 | 90.64 | **91.31** |
| **de** | 93.56 | 93.87 | **93.77** |
| **en** | 94.01 | 95.10 | **94.55** |
| **es** | 92.58 | 89.13 | **90.82** |
| **fr** | 93.23 | 88.68 | **90.90** |
| **it** | 90.23 | 92.60 | **93.40** |
| **nl** | 93.61 | 91.36 | **92.47** |
| **pl** | 92.51 | 90.81 | **91.66** |
| **pt** | 93.29 | 90.22 | **91.73** |
| **ru** | 92.37 | 92.91 | **92.64** |
| **zh** | 83.23 | 81.55 | **82.38** |
## 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.
## Usage
To use this model for inference, first install the `span_marker` library:
```bash
pip install span_marker
```
You can then run inference with this model like so:
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-base-multinerd")
# Run inference
entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
```
See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.
## Contributions
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.