span-marker pytorch safetensors token-classification ner named-entity-recognition generated_from_span_marker_trainer en dataset:DFKI-SLT/few-nerd base_model:FacebookAI/roberta-large base_model:finetune:FacebookAI/roberta-large license:cc-by-sa-4.0 model-index region:us
---
base_model: roberta-large
datasets:
- DFKI-SLT/few-nerd
language:
- en
library_name: span-marker
license: cc-by-sa-4.0
metrics:
- f1
- recall
- precision
pipeline_tag: token-classification
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
widget:
- text: Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic
    to Paris.
  example_title: Amelia Earhart
- text: Leonardo da Vinci painted the Mona Lisa based on Italian noblewoman Lisa del
    Giocondo.
  example_title: Leonardo da Vinci
- text: Most of the Steven Seagal movie ``Under Siege`` (co-starring Tommy Lee Jones)
    was filmed aboard the Battleship USS Alabama, which is docked on Mobile Bay at
    Battleship Memorial Park and open to the public.
  example_title: Under Siege
model-index:
- name: SpanMarker w. roberta-large on finegrained, supervised FewNERD by Tom Aarsen
  results:
  - task:
      type: token-classification
      name: Named Entity Recognition
    dataset:
      name: finegrained, supervised FewNERD
      type: DFKI-SLT/few-nerd
      config: supervised
      split: test
      revision: 2e3e727c63604fbfa2ff4cc5055359c84fe5ef2c
    metrics:
    - type: f1
      value: 0.7103
      name: F1
    - type: precision
      value: 0.7136
      name: Precision
    - type: recall
      value: 0.707
      name: Recall
---

# SpanMarker with roberta-large 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 [roberta-large](https://huggingface.co/roberta-large) as the underlying encoder. See [train.py](train.py) for the training script.

## Model Details

### Model Description

- **Model Type:** SpanMarker
- **Encoder:** [roberta-large](https://huggingface.co/roberta-large)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd)
- **Language:** en
- **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                     | "Street Cents", "The Gale Storm Show : Oh , Susanna", "Corazones"                                        |
| art-film                                 | "Shawshank Redemption", "Bosch", "L'Atlantide"                                                           |
| art-music                                | "Hollywood Studio Symphony", "Champion Lover", "Atkinson , Danko and Ford ( with Brockie and Hilton )"   |
| art-other                                | "Aphrodite of Milos", "Venus de Milo", "The Today Show"                                                  |
| art-painting                             | "Production/Reproduction", "Cofiwch Dryweryn", "Touit"                                                   |
| art-writtenart                           | "Imelda de ' Lambertazzi", "Time", "The Seven Year Itch"                                                 |
| building-airport                         | "Sheremetyevo International Airport", "Newark Liberty International Airport", "Luton Airport"            |
| building-hospital                        | "Memorial Sloan-Kettering Cancer Center", "Hokkaido University Hospital", "Yeungnam University Hospital" |
| building-hotel                           | "Flamingo Hotel", "The Standard Hotel", "Radisson Blu Sea Plaza Hotel"                                   |
| building-library                         | "British Library", "Berlin State Library", "Bayerische Staatsbibliothek"                                 |
| building-other                           | "Alpha Recording Studios", "Henry Ford Museum", "Communiplex"                                            |
| building-restaurant                      | "Fatburger", "Carnegie Deli", "Trumbull"                                                                 |
| building-sportsfacility                  | "Sports Center", "Glenn Warner Soccer Facility", "Boston Garden"                                         |
| building-theater                         | "Pittsburgh Civic Light Opera", "National Paris Opera", "Sanders Theatre"                                |
| event-attack/battle/war/militaryconflict | "Jurist", "Vietnam War", "Easter Offensive"                                                              |
| event-disaster                           | "the 1912 North Mount Lyell Disaster", "1990s North Korean famine", "1693 Sicily earthquake"             |
| event-election                           | "March 1898 elections", "Elections to the European Parliament", "1982 Mitcham and Morden by-election"    |
| event-other                              | "Eastwood Scoring Stage", "Union for a Popular Movement", "Masaryk Democratic Movement"                  |
| event-protest                            | "Russian Revolution", "French Revolution", "Iranian Constitutional Revolution"                           |
| event-sportsevent                        | "World Cup", "Stanley Cup", "National Champions"                                                         |
| location-GPE                             | "Croatian", "the Republic of Croatia", "Mediterranean Basin"                                             |
| location-bodiesofwater                   | "Arthur Kill", "Norfolk coast", "Atatürk Dam Lake"                                                       |
| location-island                          | "new Samsat district", "Staten Island", "Laccadives"                                                     |
| location-mountain                        | "Ruweisat Ridge", "Salamander Glacier", "Miteirya Ridge"                                                 |
| location-other                           | "Northern City Line", "Victoria line", "Cartuther"                                                       |
| location-park                            | "Gramercy Park", "Shenandoah National Park", "Painted Desert Community Complex Historic District"        |
| location-road/railway/highway/transit    | "NJT", "Friern Barnet Road", "Newark-Elizabeth Rail Link"                                                |
| 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", "Congregazione dei Nobili", "Diet"                                                      |
| organization-media/newspaper             | "Al Jazeera", "Clash", "TimeOut Melbourne"                                                               |
| organization-other                       | "IAEA", "4th Army", "Defence Sector C"                                                                   |
| organization-politicalparty              | "Al Wafa ' Islamic", "Kenseitō", "Shimpotō"                                                              |
| organization-religion                    | "Jewish", "UPCUSA", "Christian"                                                                          |
| organization-showorganization            | "Mr. Mister", "Lizzy", "Bochumer Symphoniker"                                                            |
| organization-sportsleague                | "China League One", "NHL", "First Division"                                                              |
| organization-sportsteam                  | "Arsenal", "Luc Alphand Aventures", "Tottenham"                                                          |
| other-astronomything                     | "Algol", "`` Caput Larvae ''", "Zodiac"                                                                  |
| other-award                              | "GCON", "Grand Commander of the Order of the Niger", "Order of the Republic of Guinea and Nigeria"       |
| other-biologything                       | "BAR", "N-terminal lipid", "Amphiphysin"                                                                 |
| other-chemicalthing                      | "carbon dioxide", "sulfur", "uranium"                                                                    |
| other-currency                           | "$", "Travancore Rupee", "lac crore"                                                                     |
| other-disease                            | "bladder cancer", "French Dysentery Epidemic of 1779", "hypothyroidism"                                  |
| other-educationaldegree                  | "Bachelor", "Master", "BSc ( Hons ) in physics"                                                          |
| other-god                                | "El", "Fujin", "Raijin"                                                                                  |
| other-language                           | "Latin", "Breton-speaking", "English"                                                                    |
| other-law                                | "Leahy–Smith America Invents Act ( AIA", "Thirty Years ' Peace", "United States Freedom Support Act"     |
| other-livingthing                        | "monkeys", "patchouli", "insects"                                                                        |
| other-medical                            | "Pediatrics", "pediatrician", "amitriptyline"                                                            |
| person-actor                             | "Tchéky Karyo", "Ellaline Terriss", "Edmund Payne"                                                       |
| person-artist/author                     | "George Axelrod", "Gaetano Donizett", "Hicks"                                                            |
| person-athlete                           | "Jaguar", "Tozawa", "Neville"                                                                            |
| person-director                          | "Bob Swaim", "Frank Darabont", "Richard Quine"                                                           |
| person-other                             | "Richard Benson", "Holden", "Campbell"                                                                   |
| person-politician                        | "Emeric", "Rivière", "William"                                                                           |
| person-scholar                           | "Stalmine", "Stedman", "Wurdack"                                                                         |
| person-soldier                           | "Helmuth Weidling", "Joachim Ziegler", "Krukenberg"                                                      |
| product-airplane                         | "Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS"                                                          |
| product-car                              | "100EX", "Phantom", "Corvettes - GT1 C6R"                                                                |
| product-food                             | "red grape", "yakiniku", "V. labrusca"                                                                   |
| product-game                             | "Airforce Delta", "Splinter Cell", "Hardcore RPG"                                                        |
| product-other                            | "Fairbottom Bobs", "X11", "PDP-1"                                                                        |
| product-ship                             | "HMS `` Chinkara ''", "Congress", "Essex"                                                                |
| product-software                         | "Wikipedia", "Apdf", "AmiPDF"                                                                            |
| product-train                            | "Royal Scots Grey", "High Speed Trains", "55022"                                                         |
| product-weapon                           | "AR-15 's", "ZU-23-2M Wróbel", "ZU-23-2MR Wróbel II"                                                     |

## Uses

### Direct Use

```python
from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-roberta-large-fewnerd-fine-super")
# Run inference
entities = model.predict("Most of the Steven Seagal movie ``Under Siege`` (co-starring Tommy Lee Jones) was filmed aboard the Battleship USS Alabama, 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-roberta-large-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-roberta-large-fewnerd-fine-super-finetuned")
```
</details>

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 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: 1.3.1.dev
- Transformers : 4.29.2
- PyTorch: 2.0.1+cu118
- Datasets: 2.14.3
- Tokenizers: 0.13.2