
# SpanMarker with bert-base-multilingual-cased on FewNERD

This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) as the underlying encoder.

## Model Details

### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd)
- **Languages:** en, multilingual
- **License:** cc-by-sa-4.0

### Model Sources

- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)

### Model Labels
| Label                                    | Examples                                                                                                 |
|:-----------------------------------------|:---------------------------------------------------------------------------------------------------------|
| art-broadcastprogram                     | "Corazones", "Street Cents", "The Gale Storm Show : Oh , Susanna"                                        |
| art-film                                 | "L'Atlantide", "Bosch", "Shawshank Redemption"                                                           |
| art-music                                | "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Hollywood Studio Symphony", "Champion Lover"   |
| art-other                                | "Aphrodite of Milos", "The Today Show", "Venus de Milo"                                                  |
| art-painting                             | "Production/Reproduction", "Touit", "Cofiwch Dryweryn"                                                   |
| art-writtenart                           | "The Seven Year Itch", "Time", "Imelda de ' Lambertazzi"                                                 |
| building-airport                         | "Luton Airport", "Newark Liberty International Airport", "Sheremetyevo International Airport"            |
| building-hospital                        | "Hokkaido University Hospital", "Yeungnam University Hospital", "Memorial Sloan-Kettering Cancer Center" |
| building-hotel                           | "Flamingo Hotel", "The Standard Hotel", "Radisson Blu Sea Plaza Hotel"                                   |
| building-library                         | "British Library", "Bayerische Staatsbibliothek", "Berlin State Library"                                 |
| building-other                           | "Communiplex", "Henry Ford Museum", "Alpha Recording Studios"                                            |
| building-restaurant                      | "Fatburger", "Carnegie Deli", "Trumbull"                                                                 |
| building-sportsfacility                  | "Sports Center", "Glenn Warner Soccer Facility", "Boston Garden"                                         |
| building-theater                         | "Sanders Theatre", "Pittsburgh Civic Light Opera", "National Paris Opera"                                |
| event-attack/battle/war/militaryconflict | "Vietnam War", "Jurist", "Easter Offensive"                                                              |
| event-disaster                           | "1693 Sicily earthquake", "the 1912 North Mount Lyell Disaster", "1990s North Korean famine"             |
| event-election                           | "March 1898 elections", "1982 Mitcham and Morden by-election", "Elections to the European Parliament"    |
| event-other                              | "Eastwood Scoring Stage", "Masaryk Democratic Movement", "Union for a Popular Movement"                  |
| event-protest                            | "Russian Revolution", "Iranian Constitutional Revolution", "French Revolution"                           |
| event-sportsevent                        | "Stanley Cup", "World Cup", "National Champions"                                                         |
| location-GPE                             | "Mediterranean Basin", "Croatian", "the Republic of Croatia"                                             |
| location-bodiesofwater                   | "Norfolk coast", "Atatürk Dam Lake", "Arthur Kill"                                                       |
| location-island                          | "Staten Island", "Laccadives", "new Samsat district"                                                     |
| location-mountain                        | "Miteirya Ridge", "Ruweisat Ridge", "Salamander Glacier"                                                 |
| location-other                           | "Victoria line", "Cartuther", "Northern City Line"                                                       |
| location-park                            | "Painted Desert Community Complex Historic District", "Shenandoah National Park", "Gramercy Park"        |
| location-road/railway/highway/transit    | "Friern Barnet Road", "Newark-Elizabeth Rail Link", "NJT"                                                |
| organization-company                     | "Church 's Chicken", "Dixy Chicken", "Texas Chicken"                                                     |
| organization-education                   | "MIT", "Barnard College", "Belfast Royal Academy and the Ulster College of Physical Education"           |
| organization-government/governmentagency | "Supreme Court", "Diet", "Congregazione dei Nobili"                                                      |
| organization-media/newspaper             | "TimeOut Melbourne", "Clash", "Al Jazeera"                                                               |
| organization-other                       | "IAEA", "Defence Sector C", "4th Army"                                                                   |
| organization-politicalparty              | "Al Wafa ' Islamic", "Kenseitō", "Shimpotō"                                                              |
| organization-religion                    | "Christian", "UPCUSA", "Jewish"                                                                          |
| organization-showorganization            | "Lizzy", "Mr. Mister", "Bochumer Symphoniker"                                                            |
| organization-sportsleague                | "China League One", "NHL", "First Division"                                                              |
| organization-sportsteam                  | "Luc Alphand Aventures", "Tottenham", "Arsenal"                                                          |
| other-astronomything                     | "`` Caput Larvae ''", "Algol", "Zodiac"                                                                  |
| other-award                              | "GCON", "Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger"       |
| other-biologything                       | "BAR", "Amphiphysin", "N-terminal lipid"                                                                 |
| other-chemicalthing                      | "sulfur", "uranium", "carbon dioxide"                                                                    |
| other-currency                           | "Travancore Rupee", "$", "lac crore"                                                                     |
| other-disease                            | "bladder cancer", "hypothyroidism", "French Dysentery Epidemic of 1779"                                  |
| other-educationaldegree                  | "Master", "Bachelor", "BSc ( Hons ) in physics"                                                          |
| other-god                                | "Fujin", "Raijin", "El"                                                                                  |
| other-language                           | "Latin", "English", "Breton-speaking"                                                                    |
| other-law                                | "Thirty Years ' Peace", "United States Freedom Support Act", "Leahy–Smith America Invents Act ( AIA"     |
| other-livingthing                        | "monkeys", "insects", "patchouli"                                                                        |
| other-medical                            | "Pediatrics", "amitriptyline", "pediatrician"                                                            |
| person-actor                             | "Edmund Payne", "Ellaline Terriss", "Tchéky Karyo"                                                       |
| person-artist/author                     | "George Axelrod", "Hicks", "Gaetano Donizett"                                                            |
| person-athlete                           | "Tozawa", "Neville", "Jaguar"                                                                            |
| person-director                          | "Richard Quine", "Frank Darabont", "Bob Swaim"                                                           |
| person-other                             | "Richard Benson", "Campbell", "Holden"                                                                   |
| person-politician                        | "Rivière", "William", "Emeric"                                                                           |
| person-scholar                           | "Wurdack", "Stedman", "Stalmine"                                                                         |
| person-soldier                           | "Joachim Ziegler", "Krukenberg", "Helmuth Weidling"                                                      |
| product-airplane                         | "Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS"                                                          |
| product-car                              | "Corvettes - GT1 C6R", "Phantom", "100EX"                                                                |
| product-food                             | "V. labrusca", "yakiniku", "red grape"                                                                   |
| product-game                             | "Airforce Delta", "Hardcore RPG", "Splinter Cell"                                                        |
| product-other                            | "PDP-1", "Fairbottom Bobs", "X11"                                                                        |
| product-ship                             | "HMS `` Chinkara ''", "Congress", "Essex"                                                                |
| product-software                         | "Apdf", "Wikipedia", "AmiPDF"                                                                            |
| product-train                            | "Royal Scots Grey", "High Speed Trains", "55022"                                                         |
| product-weapon                           | "AR-15 's", "ZU-23-2M Wróbel", "ZU-23-2MR Wróbel II"                                                     |

## Evaluation

### Metrics
| Label                                    | Precision | Recall | F1     |
|:-----------------------------------------|:----------|:-------|:-------|
| **all**                                  | 0.7041    | 0.6973 | 0.7007 |
| art-broadcastprogram                     | 0.5863    | 0.6252 | 0.6051 |
| art-film                                 | 0.7779    | 0.752  | 0.7647 |
| art-music                                | 0.8014    | 0.7570 | 0.7786 |
| art-other                                | 0.4209    | 0.3221 | 0.3649 |
| art-painting                             | 0.5938    | 0.6667 | 0.6281 |
| art-writtenart                           | 0.6854    | 0.6415 | 0.6628 |
| building-airport                         | 0.8197    | 0.8242 | 0.8219 |
| building-hospital                        | 0.7215    | 0.8187 | 0.7671 |
| building-hotel                           | 0.7233    | 0.6906 | 0.7066 |
| building-library                         | 0.7588    | 0.7268 | 0.7424 |
| building-other                           | 0.5842    | 0.5855 | 0.5848 |
| building-restaurant                      | 0.5567    | 0.4871 | 0.5195 |
| building-sportsfacility                  | 0.6512    | 0.7690 | 0.7052 |
| building-theater                         | 0.6994    | 0.7516 | 0.7246 |
| event-attack/battle/war/militaryconflict | 0.7800    | 0.7332 | 0.7559 |
| event-disaster                           | 0.5767    | 0.5266 | 0.5505 |
| event-election                           | 0.5106    | 0.1319 | 0.2096 |
| event-other                              | 0.4931    | 0.4145 | 0.4504 |
| event-protest                            | 0.3711    | 0.4337 | 0.4000 |
| event-sportsevent                        | 0.6156    | 0.6156 | 0.6156 |
| location-GPE                             | 0.8175    | 0.8508 | 0.8338 |
| location-bodiesofwater                   | 0.7297    | 0.7622 | 0.7456 |
| location-island                          | 0.7314    | 0.6703 | 0.6995 |
| location-mountain                        | 0.7538    | 0.7283 | 0.7409 |
| location-other                           | 0.4370    | 0.3040 | 0.3585 |
| location-park                            | 0.7063    | 0.6878 | 0.6969 |
| location-road/railway/highway/transit    | 0.7092    | 0.7259 | 0.7174 |
| organization-company                     | 0.6911    | 0.6943 | 0.6927 |
| organization-education                   | 0.7799    | 0.7973 | 0.7885 |
| organization-government/governmentagency | 0.5518    | 0.4474 | 0.4942 |
| organization-media/newspaper             | 0.6268    | 0.6761 | 0.6505 |
| organization-other                       | 0.5804    | 0.5341 | 0.5563 |
| organization-politicalparty              | 0.6627    | 0.7306 | 0.6949 |
| organization-religion                    | 0.5636    | 0.6265 | 0.5934 |
| organization-showorganization            | 0.6023    | 0.6086 | 0.6054 |
| organization-sportsleague                | 0.6594    | 0.6497 | 0.6545 |
| organization-sportsteam                  | 0.7341    | 0.7703 | 0.7518 |
| other-astronomything                     | 0.7806    | 0.8289 | 0.8040 |
| other-award                              | 0.7230    | 0.6703 | 0.6957 |
| other-biologything                       | 0.6733    | 0.6366 | 0.6544 |
| other-chemicalthing                      | 0.5962    | 0.5838 | 0.5899 |
| other-currency                           | 0.7135    | 0.7822 | 0.7463 |
| other-disease                            | 0.6260    | 0.7063 | 0.6637 |
| other-educationaldegree                  | 0.6       | 0.6033 | 0.6016 |
| other-god                                | 0.7051    | 0.7118 | 0.7085 |
| other-language                           | 0.6849    | 0.7968 | 0.7366 |
| other-law                                | 0.6814    | 0.6843 | 0.6829 |
| other-livingthing                        | 0.5959    | 0.6443 | 0.6192 |
| other-medical                            | 0.5247    | 0.4811 | 0.5020 |
| person-actor                             | 0.8342    | 0.7960 | 0.8146 |
| person-artist/author                     | 0.7052    | 0.7482 | 0.7261 |
| person-athlete                           | 0.8396    | 0.8530 | 0.8462 |
| person-director                          | 0.725     | 0.7329 | 0.7289 |
| person-other                             | 0.6866    | 0.6672 | 0.6767 |
| person-politician                        | 0.6819    | 0.6852 | 0.6835 |
| person-scholar                           | 0.5468    | 0.4953 | 0.5198 |
| person-soldier                           | 0.5360    | 0.5641 | 0.5497 |
| product-airplane                         | 0.6825    | 0.6730 | 0.6777 |
| product-car                              | 0.7205    | 0.7016 | 0.7109 |
| product-food                             | 0.6036    | 0.5394 | 0.5697 |
| product-game                             | 0.7740    | 0.6876 | 0.7282 |
| product-other                            | 0.5250    | 0.4117 | 0.4615 |
| product-ship                             | 0.6781    | 0.6763 | 0.6772 |
| product-software                         | 0.6701    | 0.6603 | 0.6652 |
| product-train                            | 0.5919    | 0.6051 | 0.5984 |
| product-weapon                           | 0.6507    | 0.5433 | 0.5921 |

## Uses

### Direct Use for Inference

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-fewnerd-fine-super")
# Run inference
entities = model.predict("Most of the Steven Seagal movie \"Under Siege \"(co-starring Tommy Lee Jones) was filmed on the, which is docked on Mobile Bay at Battleship Memorial Park and open to the public.")
```

### Downstream Use
You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

```python
from span_marker import SpanMarkerModel, Trainer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-mbert-base-fewnerd-fine-super")

# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003

# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
    model=model,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("tomaarsen/span-marker-mbert-base-fewnerd-fine-super-finetuned")
```
</details>

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Set Metrics
| Training set          | Min | Median  | Max |
|:----------------------|:----|:--------|:----|
| Sentence length       | 1   | 24.4945 | 267 |
| Entities per sentence | 0   | 2.5832  | 88  |

### Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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
| Epoch  | Step  | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 0.2972 | 3000  | 0.0274          | 0.6488               | 0.6457            | 0.6473        | 0.9121              |
| 0.5944 | 6000  | 0.0252          | 0.6686               | 0.6545            | 0.6615        | 0.9160              |
| 0.8915 | 9000  | 0.0239          | 0.6918               | 0.6547            | 0.6727        | 0.9178              |
| 1.1887 | 12000 | 0.0235          | 0.6962               | 0.6727            | 0.6842        | 0.9210              |
| 1.4859 | 15000 | 0.0233          | 0.6872               | 0.6742            | 0.6806        | 0.9201              |
| 1.7831 | 18000 | 0.0226          | 0.6969               | 0.6891            | 0.6929        | 0.9236              |
| 2.0802 | 21000 | 0.0231          | 0.7030               | 0.6916            | 0.6973        | 0.9246              |
| 2.3774 | 24000 | 0.0227          | 0.7020               | 0.6936            | 0.6978        | 0.9248              |
| 2.6746 | 27000 | 0.0223          | 0.7079               | 0.6989            | 0.7034        | 0.9258              |
| 2.9718 | 30000 | 0.0222          | 0.7089               | 0.7009            | 0.7049        | 0.9263              |

### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Carbon Emitted**: 0.573 kg of CO2
- **Hours Used**: 3.867 hours

### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB

### Framework Versions
- Python: 3.9.16
- SpanMarker: 1.4.1.dev
- Transformers: 4.30.0
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.0
- Tokenizers: 0.13.2

## Citation

### BibTeX
```
@software{Aarsen_SpanMarker,
    author = {Aarsen, Tom},
    license = {Apache-2.0},
    title = {{SpanMarker for Named Entity Recognition}},
    url = {https://github.com/tomaarsen/SpanMarkerNER}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
{"language": ["en", "multilingual"], "license": "cc-by-sa-4.0", "library_name": "span-marker", "tags": ["span-marker", "token-classification", "ner", "named-entity-recognition", "generated_from_span_marker_trainer"], "datasets": ["DFKI-SLT/few-nerd"], "metrics": ["precision", "recall", "f1"], "widget": [{"text": "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.", "example_title": "English 1"}, {"text": "The WPC led the international peace movement in the decade after the Second World War, but its failure to speak out against the Soviet suppression of the 1956 Hungarian uprising and the resumption of Soviet nuclear tests in 1961 marginalised it, and in the 1960s it was eclipsed by the newer, non-aligned peace organizations like the Campaign for Nuclear Disarmament.", "example_title": "English 2"}, {"text": "Most of the Steven Seagal movie \"Under Siege\" (co-starring Tommy Lee Jones) was filmed on the Battleship USS Alabama, which is docked on Mobile Bay at Battleship Memorial Park and open to the public.", "example_title": "English 3"}, {"text": "The Central African CFA franc (French: \"franc CFA\" or simply \"franc\", ISO 4217 code: XAF) is the currency of six independent states in Central Africa: Cameroon, Central African Republic, Chad, Republic of the Congo, Equatorial Guinea and Gabon.", "example_title": "English 4"}, {"text": "Brenner conducted post-doctoral research at Brandeis University with Gregory Petsko and then took his first academic position at Thomas Jefferson University in 1996, moving to Dartmouth Medical School in 2003, where he served as Associate Director for Basic Sciences at Norris Cotton Cancer Center.", "example_title": "English 5"}, {"text": "On Friday, October 27, 2017, the Senate of Spain (Senado) voted 214 to 47 to invoke Article 155 of the Spanish Constitution over Catalonia after the Catalan Parliament declared the independence.", "example_title": "English 6"}, {"text": "Amelia Earthart vol\u00f3 su Lockheed Vega 5B monomotor a trav\u00e9s del Oc\u00e9ano Atl\u00e1ntico hasta Par\u00eds.", "example_title": "Spanish"}, {"text": "Amelia Earthart a fait voler son monomoteur Lockheed Vega 5B \u00e0 travers l'ocean Atlantique jusqu'\u00e0 Paris.", "example_title": "French"}, {"text": "Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B \u00fcber den Atlantik nach Paris.", "example_title": "German"}, {"text": "\u0410\u043c\u0435\u043b\u0438\u044f \u042d\u0440\u0442\u0445\u0430\u0440\u0442 \u043f\u0435\u0440\u0435\u043b\u0435\u0442\u0435\u043b\u0430 \u043d\u0430 \u0441\u0432\u043e\u0435\u043c \u043e\u0434\u043d\u043e\u043c\u043e\u0442\u043e\u0440\u043d\u043e\u043c \u0441\u0430\u043c\u043e\u043b\u0435\u0442\u0435 Lockheed Vega 5B \u0447\u0435\u0440\u0435\u0437 \u0410\u0442\u043b\u0430\u043d\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u043e\u043a\u0435\u0430\u043d \u0432 \u041f\u0430\u0440\u0438\u0436.", "example_title": "Russian"}, {"text": "Amelia Earthart vloog met haar \u00e9\u00e9n-motorige Lockheed Vega 5B over de Atlantische Oceaan naar Parijs.", "example_title": "Dutch"}, {"text": "Amelia Earthart przelecia\u0142a swoim jednosilnikowym samolotem Lockheed Vega 5B przez Ocean Atlantycki do Pary\u017ca.", "example_title": "Polish"}, {"text": "Amelia Earthart flaug eins hreyfils Lockheed Vega 5B yfir Atlantshafi\u00f0 til Par\u00edsar.", "example_title": "Icelandic"}, {"text": "\u0397 Amelia Earthart \u03c0\u03ad\u03c4\u03b1\u03be\u03b5 \u03c4\u03bf \u03bc\u03bf\u03bd\u03bf\u03ba\u03b9\u03bd\u03b7\u03c4\u03ae\u03c1\u03b9\u03bf Lockheed Vega 5B \u03c4\u03b7\u03c2 \u03c0\u03ad\u03c1\u03b1 \u200b\u200b\u03b1\u03c0\u03cc \u03c4\u03bf\u03bd \u0391\u03c4\u03bb\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03a9\u03ba\u03b5\u03b1\u03bd\u03cc \u03c3\u03c4\u03bf \u03a0\u03b1\u03c1\u03af\u03c3\u03b9.", "example_title": "Greek"}], "pipeline_tag": "token-classification", "co2_eq_emissions": {"emissions": 572.6675932546113, "source": "codecarbon", "training_type": "fine-tuning", "on_cloud": false, "cpu_model": "13th Gen Intel(R) Core(TM) i7-13700K", "ram_total_size": 31.777088165283203, "hours_used": 3.867, "hardware_used": "1 x NVIDIA GeForce RTX 3090"}, "base_model": "bert-base-multilingual-cased", "model-index": [{"name": "SpanMarker with bert-base-multilingual-cased on FewNERD", "results": [{"task": {"type": "token-classification", "name": "Named Entity Recognition"}, "dataset": {"name": "FewNERD", "type": "DFKI-SLT/few-nerd", "split": "test"}, "metrics": [{"type": "f1", "value": 0.7006507253689264, "name": "F1"}, {"type": "precision", "value": 0.7040676584045078, "name": "Precision"}, {"type": "recall", "value": 0.6972667978051558, "name": "Recall"}]}]}]}
span-marker pytorch tensorboard token-classification ner named-entity-recognition generated_from_span_marker_trainer en multilingual dataset:DFKI-SLT/few-nerd base_model:bert-base-multilingual-cased license:cc-by-sa-4.0 model-index co2_eq_emissions region:us