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--- |
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language: |
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- en |
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license: cc-by-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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- generated_from_span_marker_trainer |
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datasets: |
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- DFKI-SLT/few-nerd |
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metrics: |
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- f1 |
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- recall |
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- precision |
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pipeline_tag: token-classification |
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widget: |
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- text: Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic |
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to Paris. |
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example_title: Amelia Earhart |
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- text: Leonardo da Vinci painted the Mona Lisa based on Italian noblewoman |
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Lisa del Giocondo. |
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example_title: Leonardo da Vinci |
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- text: Most of the Steven Seagal movie ``Under Siege`` (co-starring Tommy Lee Jones) |
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was filmed aboard the Battleship USS Alabama, which is docked on Mobile Bay at |
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Battleship Memorial Park and open to the public. |
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example_title: Under Siege |
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base_model: roberta-large |
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model-index: |
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- name: SpanMarker w. roberta-large on finegrained, supervised FewNERD 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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name: finegrained, supervised FewNERD |
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type: DFKI-SLT/few-nerd |
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config: supervised |
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split: test |
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revision: 2e3e727c63604fbfa2ff4cc5055359c84fe5ef2c |
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metrics: |
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- type: f1 |
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value: 0.7103 |
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name: F1 |
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- type: precision |
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value: 0.7136 |
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name: Precision |
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- type: recall |
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value: 0.707 |
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name: Recall |
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--- |
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# SpanMarker with roberta-large on FewNERD |
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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. |
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## Model Details |
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### Model Description |
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- **Model Type:** SpanMarker |
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- **Encoder:** [roberta-large](https://huggingface.co/roberta-large) |
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- **Maximum Sequence Length:** 256 tokens |
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- **Maximum Entity Length:** 8 words |
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- **Training Dataset:** [FewNERD](https://huggingface.co/datasets/DFKI-SLT/few-nerd) |
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- **Language:** en |
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- **License:** cc-by-sa-4.0 |
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### Model Sources |
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- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER) |
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- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf) |
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### Model Labels |
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| Label | Examples | |
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|:-----------------------------------------|:---------------------------------------------------------------------------------------------------------| |
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| art-broadcastprogram | "Street Cents", "The Gale Storm Show : Oh , Susanna", "Corazones" | |
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| art-film | "Shawshank Redemption", "Bosch", "L'Atlantide" | |
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| art-music | "Hollywood Studio Symphony", "Champion Lover", "Atkinson , Danko and Ford ( with Brockie and Hilton )" | |
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| art-other | "Aphrodite of Milos", "Venus de Milo", "The Today Show" | |
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| art-painting | "Production/Reproduction", "Cofiwch Dryweryn", "Touit" | |
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| art-writtenart | "Imelda de ' Lambertazzi", "Time", "The Seven Year Itch" | |
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| building-airport | "Sheremetyevo International Airport", "Newark Liberty International Airport", "Luton Airport" | |
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| building-hospital | "Memorial Sloan-Kettering Cancer Center", "Hokkaido University Hospital", "Yeungnam University Hospital" | |
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| building-hotel | "Flamingo Hotel", "The Standard Hotel", "Radisson Blu Sea Plaza Hotel" | |
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| building-library | "British Library", "Berlin State Library", "Bayerische Staatsbibliothek" | |
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| building-other | "Alpha Recording Studios", "Henry Ford Museum", "Communiplex" | |
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| building-restaurant | "Fatburger", "Carnegie Deli", "Trumbull" | |
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| building-sportsfacility | "Sports Center", "Glenn Warner Soccer Facility", "Boston Garden" | |
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| building-theater | "Pittsburgh Civic Light Opera", "National Paris Opera", "Sanders Theatre" | |
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| event-attack/battle/war/militaryconflict | "Jurist", "Vietnam War", "Easter Offensive" | |
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| event-disaster | "the 1912 North Mount Lyell Disaster", "1990s North Korean famine", "1693 Sicily earthquake" | |
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| event-election | "March 1898 elections", "Elections to the European Parliament", "1982 Mitcham and Morden by-election" | |
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| event-other | "Eastwood Scoring Stage", "Union for a Popular Movement", "Masaryk Democratic Movement" | |
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| event-protest | "Russian Revolution", "French Revolution", "Iranian Constitutional Revolution" | |
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| event-sportsevent | "World Cup", "Stanley Cup", "National Champions" | |
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| location-GPE | "Croatian", "the Republic of Croatia", "Mediterranean Basin" | |
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| location-bodiesofwater | "Arthur Kill", "Norfolk coast", "Atatürk Dam Lake" | |
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| location-island | "new Samsat district", "Staten Island", "Laccadives" | |
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| location-mountain | "Ruweisat Ridge", "Salamander Glacier", "Miteirya Ridge" | |
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| location-other | "Northern City Line", "Victoria line", "Cartuther" | |
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| location-park | "Gramercy Park", "Shenandoah National Park", "Painted Desert Community Complex Historic District" | |
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| location-road/railway/highway/transit | "NJT", "Friern Barnet Road", "Newark-Elizabeth Rail Link" | |
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| organization-company | "Church 's Chicken", "Dixy Chicken", "Texas Chicken" | |
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| organization-education | "MIT", "Barnard College", "Belfast Royal Academy and the Ulster College of Physical Education" | |
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| organization-government/governmentagency | "Supreme Court", "Congregazione dei Nobili", "Diet" | |
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| organization-media/newspaper | "Al Jazeera", "Clash", "TimeOut Melbourne" | |
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| organization-other | "IAEA", "4th Army", "Defence Sector C" | |
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| organization-politicalparty | "Al Wafa ' Islamic", "Kenseitō", "Shimpotō" | |
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| organization-religion | "Jewish", "UPCUSA", "Christian" | |
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| organization-showorganization | "Mr. Mister", "Lizzy", "Bochumer Symphoniker" | |
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| organization-sportsleague | "China League One", "NHL", "First Division" | |
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| organization-sportsteam | "Arsenal", "Luc Alphand Aventures", "Tottenham" | |
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| other-astronomything | "Algol", "`` Caput Larvae ''", "Zodiac" | |
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| other-award | "GCON", "Grand Commander of the Order of the Niger", "Order of the Republic of Guinea and Nigeria" | |
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| other-biologything | "BAR", "N-terminal lipid", "Amphiphysin" | |
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| other-chemicalthing | "carbon dioxide", "sulfur", "uranium" | |
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| other-currency | "$", "Travancore Rupee", "lac crore" | |
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| other-disease | "bladder cancer", "French Dysentery Epidemic of 1779", "hypothyroidism" | |
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| other-educationaldegree | "Bachelor", "Master", "BSc ( Hons ) in physics" | |
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| other-god | "El", "Fujin", "Raijin" | |
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| other-language | "Latin", "Breton-speaking", "English" | |
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| other-law | "Leahy–Smith America Invents Act ( AIA", "Thirty Years ' Peace", "United States Freedom Support Act" | |
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| other-livingthing | "monkeys", "patchouli", "insects" | |
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| other-medical | "Pediatrics", "pediatrician", "amitriptyline" | |
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| person-actor | "Tchéky Karyo", "Ellaline Terriss", "Edmund Payne" | |
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| person-artist/author | "George Axelrod", "Gaetano Donizett", "Hicks" | |
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| person-athlete | "Jaguar", "Tozawa", "Neville" | |
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| person-director | "Bob Swaim", "Frank Darabont", "Richard Quine" | |
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| person-other | "Richard Benson", "Holden", "Campbell" | |
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| person-politician | "Emeric", "Rivière", "William" | |
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| person-scholar | "Stalmine", "Stedman", "Wurdack" | |
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| person-soldier | "Helmuth Weidling", "Joachim Ziegler", "Krukenberg" | |
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| product-airplane | "Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS" | |
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| product-car | "100EX", "Phantom", "Corvettes - GT1 C6R" | |
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| product-food | "red grape", "yakiniku", "V. labrusca" | |
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| product-game | "Airforce Delta", "Splinter Cell", "Hardcore RPG" | |
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| product-other | "Fairbottom Bobs", "X11", "PDP-1" | |
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| product-ship | "HMS `` Chinkara ''", "Congress", "Essex" | |
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| product-software | "Wikipedia", "Apdf", "AmiPDF" | |
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| product-train | "Royal Scots Grey", "High Speed Trains", "55022" | |
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| product-weapon | "AR-15 's", "ZU-23-2M Wróbel", "ZU-23-2MR Wróbel II" | |
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## Uses |
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### Direct Use |
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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-roberta-large-fewnerd-fine-super") |
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# Run inference |
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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.") |
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``` |
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### Downstream Use |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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```python |
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from span_marker import SpanMarkerModel, Trainer |
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# Download from the 🤗 Hub |
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model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-roberta-large-fewnerd-fine-super") |
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# Specify a Dataset with "tokens" and "ner_tag" columns |
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dataset = load_dataset("conll2003") # For example CoNLL2003 |
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# Initialize a Trainer using the pretrained model & dataset |
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trainer = Trainer( |
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model=model, |
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train_dataset=dataset["train"], |
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eval_dataset=dataset["validation"], |
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) |
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trainer.train() |
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trainer.save_model("tomaarsen/span-marker-roberta-large-fewnerd-fine-super-finetuned") |
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``` |
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</details> |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:----------------------|:----|:--------|:----| |
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| Sentence length | 1 | 24.4945 | 267 | |
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| Entities per sentence | 0 | 2.5832 | 88 | |
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### Training Hyperparameters |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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: 3 |
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### Training Hardware |
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- **On Cloud**: No |
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- **GPU Model**: 1 x NVIDIA GeForce RTX 3090 |
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- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K |
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- **RAM Size**: 31.78 GB |
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### Framework Versions |
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- Python: 3.9.16 |
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- SpanMarker: 1.3.1.dev |
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- Transformers : 4.29.2 |
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- PyTorch: 2.0.1+cu118 |
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- Datasets: 2.14.3 |
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- Tokenizers: 0.13.2 |
