--- 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 --- # 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.
Click to expand ```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") ```
## 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