| --- |
| language: |
| - en |
| 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: |
| - f1 |
| - recall |
| - precision |
| pipeline_tag: token-classification |
| widget: |
| - text: Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic |
| to Paris. |
| example_title: Amelia Earhart |
| - text: Leonardo di ser Piero da Vinci painted the Mona Lisa based on Italian noblewoman |
| Lisa del Giocondo. |
| example_title: Leonardo da Vinci |
| base_model: bert-base-cased |
| model-index: |
| - name: SpanMarker w. bert-base-cased 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: 0.7053 |
| name: F1 |
| - type: precision |
| value: 0.7101 |
| name: Precision |
| - type: recall |
| value: 0.7005 |
| name: Recall |
| --- |
| <span style="color:red; font-size:20px" ><b> |
| Attention! This is a proof-of-concept model deployed here just for research demonstration. |
| Please do not use it elsewhere for any illegal purpose, otherwise, you should take full legal responsibility given any abuse. |
| </b></span> |
|
|
| # SpanMarker with bert-base-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-cased](https://huggingface.co/bert-base-cased) as the underlying encoder. |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| - **Model Type:** SpanMarker |
| - **Encoder:** [bert-base-cased](https://huggingface.co/bert-base-cased) |
| - **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", "Corazones", "The Gale Storm Show : Oh , Susanna" | |
| | art-film | "Bosch", "L'Atlantide", "Shawshank Redemption" | |
| | art-music | "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Champion Lover", "Hollywood Studio Symphony" | |
| | art-other | "Aphrodite of Milos", "Venus de Milo", "The Today Show" | |
| | art-painting | "Production/Reproduction", "Touit", "Cofiwch Dryweryn" | |
| | art-writtenart | "Imelda de ' Lambertazzi", "Time", "The Seven Year Itch" | |
| | 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 | "The Standard Hotel", "Radisson Blu Sea Plaza Hotel", "Flamingo Hotel" | |
| | building-library | "British Library", "Berlin State Library", "Bayerische Staatsbibliothek" | |
| | building-other | "Communiplex", "Alpha Recording Studios", "Henry Ford Museum" | |
| | building-restaurant | "Fatburger", "Carnegie Deli", "Trumbull" | |
| | building-sportsfacility | "Glenn Warner Soccer Facility", "Boston Garden", "Sports Center" | |
| | building-theater | "Pittsburgh Civic Light Opera", "Sanders Theatre", "National Paris Opera" | |
| | event-attack/battle/war/militaryconflict | "Easter Offensive", "Vietnam War", "Jurist" | |
| | event-disaster | "the 1912 North Mount Lyell Disaster", "1693 Sicily earthquake", "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", "Union for a Popular Movement", "Masaryk Democratic Movement" | |
| | event-protest | "French Revolution", "Russian Revolution", "Iranian Constitutional Revolution" | |
| | event-sportsevent | "National Champions", "World Cup", "Stanley Cup" | |
| | location-GPE | "Mediterranean Basin", "the Republic of Croatia", "Croatian" | |
| | location-bodiesofwater | "Atatürk Dam Lake", "Norfolk coast", "Arthur Kill" | |
| | location-island | "Laccadives", "Staten Island", "new Samsat district" | |
| | location-mountain | "Salamander Glacier", "Miteirya Ridge", "Ruweisat Ridge" | |
| | location-other | "Northern City Line", "Victoria line", "Cartuther" | |
| | location-park | "Gramercy Park", "Painted Desert Community Complex Historic District", "Shenandoah National Park" | |
| | location-road/railway/highway/transit | "Friern Barnet Road", "Newark-Elizabeth Rail Link", "NJT" | |
| | organization-company | "Dixy Chicken", "Texas Chicken", "Church 's Chicken" | |
| | organization-education | "MIT", "Belfast Royal Academy and the Ulster College of Physical Education", "Barnard College" | |
| | organization-government/governmentagency | "Congregazione dei Nobili", "Diet", "Supreme Court" | |
| | organization-media/newspaper | "TimeOut Melbourne", "Clash", "Al Jazeera" | |
| | organization-other | "Defence Sector C", "IAEA", "4th Army" | |
| | organization-politicalparty | "Shimpotō", "Al Wafa ' Islamic", "Kenseitō" | |
| | organization-religion | "Jewish", "Christian", "UPCUSA" | |
| | organization-showorganization | "Lizzy", "Bochumer Symphoniker", "Mr. Mister" | |
| | organization-sportsleague | "China League One", "First Division", "NHL" | |
| | organization-sportsteam | "Tottenham", "Arsenal", "Luc Alphand Aventures" | |
| | other-astronomything | "Zodiac", "Algol", "`` Caput Larvae ''" | |
| | other-award | "GCON", "Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger" | |
| | other-biologything | "N-terminal lipid", "BAR", "Amphiphysin" | |
| | other-chemicalthing | "uranium", "carbon dioxide", "sulfur" | |
| | other-currency | "$", "Travancore Rupee", "lac crore" | |
| | other-disease | "French Dysentery Epidemic of 1779", "hypothyroidism", "bladder cancer" | |
| | other-educationaldegree | "Master", "Bachelor", "BSc ( Hons ) in physics" | |
| | other-god | "El", "Fujin", "Raijin" | |
| | other-language | "Breton-speaking", "English", "Latin" | |
| | other-law | "Thirty Years ' Peace", "Leahy–Smith America Invents Act ( AIA", "United States Freedom Support Act" | |
| | other-livingthing | "insects", "monkeys", "patchouli" | |
| | other-medical | "Pediatrics", "amitriptyline", "pediatrician" | |
| | person-actor | "Ellaline Terriss", "Tchéky Karyo", "Edmund Payne" | |
| | person-artist/author | "George Axelrod", "Gaetano Donizett", "Hicks" | |
| | person-athlete | "Jaguar", "Neville", "Tozawa" | |
| | person-director | "Bob Swaim", "Richard Quine", "Frank Darabont" | |
| | person-other | "Richard Benson", "Holden", "Campbell" | |
| | person-politician | "William", "Rivière", "Emeric" | |
| | person-scholar | "Stedman", "Wurdack", "Stalmine" | |
| | person-soldier | "Helmuth Weidling", "Krukenberg", "Joachim Ziegler" | |
| | product-airplane | "Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS" | |
| | product-car | "100EX", "Corvettes - GT1 C6R", "Phantom" | |
| | product-food | "red grape", "yakiniku", "V. labrusca" | |
| | product-game | "Airforce Delta", "Hardcore RPG", "Splinter Cell" | |
| | product-other | "Fairbottom Bobs", "X11", "PDP-1" | |
| | product-ship | "Congress", "Essex", "HMS `` Chinkara ''" | |
| | product-software | "AmiPDF", "Apdf", "Wikipedia" | |
| | product-train | "High Speed Trains", "55022", "Royal Scots Grey" | |
| | 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-bert-base-fewnerd-fine-super") |
| # Run inference |
| entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") |
| ``` |
|
|
| ### 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-bert-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-bert-base-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: 5e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - 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 |
