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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 di ser Piero 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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base_model: bert-base-cased |
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model-index: |
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- name: SpanMarker w. bert-base-cased 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: 0.7053 |
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name: F1 |
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- type: precision |
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value: 0.7101 |
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name: Precision |
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- type: recall |
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value: 0.7005 |
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name: Recall |
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--- |
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# SpanMarker with bert-base-cased 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 [bert-base-cased](https://huggingface.co/bert-base-cased) as the underlying encoder. |
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## Model Details |
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### Model Description |
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- **Model Type:** SpanMarker |
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- **Encoder:** [bert-base-cased](https://huggingface.co/bert-base-cased) |
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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", "Corazones", "The Gale Storm Show : Oh , Susanna" | |
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| art-film | "Bosch", "L'Atlantide", "Shawshank Redemption" | |
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| art-music | "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Champion Lover", "Hollywood Studio Symphony" | |
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| art-other | "Aphrodite of Milos", "Venus de Milo", "The Today Show" | |
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| art-painting | "Production/Reproduction", "Touit", "Cofiwch Dryweryn" | |
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| art-writtenart | "Imelda de ' Lambertazzi", "Time", "The Seven Year Itch" | |
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| building-airport | "Luton Airport", "Newark Liberty International Airport", "Sheremetyevo International Airport" | |
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| building-hospital | "Hokkaido University Hospital", "Yeungnam University Hospital", "Memorial Sloan-Kettering Cancer Center" | |
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| building-hotel | "The Standard Hotel", "Radisson Blu Sea Plaza Hotel", "Flamingo Hotel" | |
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| building-library | "British Library", "Berlin State Library", "Bayerische Staatsbibliothek" | |
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| building-other | "Communiplex", "Alpha Recording Studios", "Henry Ford Museum" | |
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| building-restaurant | "Fatburger", "Carnegie Deli", "Trumbull" | |
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| building-sportsfacility | "Glenn Warner Soccer Facility", "Boston Garden", "Sports Center" | |
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| building-theater | "Pittsburgh Civic Light Opera", "Sanders Theatre", "National Paris Opera" | |
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| event-attack/battle/war/militaryconflict | "Easter Offensive", "Vietnam War", "Jurist" | |
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| event-disaster | "the 1912 North Mount Lyell Disaster", "1693 Sicily earthquake", "1990s North Korean famine" | |
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| event-election | "March 1898 elections", "1982 Mitcham and Morden by-election", "Elections to the European Parliament" | |
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| event-other | "Eastwood Scoring Stage", "Union for a Popular Movement", "Masaryk Democratic Movement" | |
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| event-protest | "French Revolution", "Russian Revolution", "Iranian Constitutional Revolution" | |
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| event-sportsevent | "National Champions", "World Cup", "Stanley Cup" | |
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| location-GPE | "Mediterranean Basin", "the Republic of Croatia", "Croatian" | |
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| location-bodiesofwater | "Atatürk Dam Lake", "Norfolk coast", "Arthur Kill" | |
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| location-island | "Laccadives", "Staten Island", "new Samsat district" | |
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| location-mountain | "Salamander Glacier", "Miteirya Ridge", "Ruweisat Ridge" | |
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| location-other | "Northern City Line", "Victoria line", "Cartuther" | |
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| location-park | "Gramercy Park", "Painted Desert Community Complex Historic District", "Shenandoah National Park" | |
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| location-road/railway/highway/transit | "Friern Barnet Road", "Newark-Elizabeth Rail Link", "NJT" | |
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| organization-company | "Dixy Chicken", "Texas Chicken", "Church 's Chicken" | |
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| organization-education | "MIT", "Belfast Royal Academy and the Ulster College of Physical Education", "Barnard College" | |
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| organization-government/governmentagency | "Congregazione dei Nobili", "Diet", "Supreme Court" | |
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| organization-media/newspaper | "TimeOut Melbourne", "Clash", "Al Jazeera" | |
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| organization-other | "Defence Sector C", "IAEA", "4th Army" | |
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| organization-politicalparty | "Shimpotō", "Al Wafa ' Islamic", "Kenseitō" | |
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| organization-religion | "Jewish", "Christian", "UPCUSA" | |
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| organization-showorganization | "Lizzy", "Bochumer Symphoniker", "Mr. Mister" | |
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| organization-sportsleague | "China League One", "First Division", "NHL" | |
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| organization-sportsteam | "Tottenham", "Arsenal", "Luc Alphand Aventures" | |
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| other-astronomything | "Zodiac", "Algol", "`` Caput Larvae ''" | |
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| other-award | "GCON", "Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger" | |
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| other-biologything | "N-terminal lipid", "BAR", "Amphiphysin" | |
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| other-chemicalthing | "uranium", "carbon dioxide", "sulfur" | |
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| other-currency | "$", "Travancore Rupee", "lac crore" | |
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| other-disease | "French Dysentery Epidemic of 1779", "hypothyroidism", "bladder cancer" | |
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| other-educationaldegree | "Master", "Bachelor", "BSc ( Hons ) in physics" | |
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| other-god | "El", "Fujin", "Raijin" | |
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| other-language | "Breton-speaking", "English", "Latin" | |
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| other-law | "Thirty Years ' Peace", "Leahy–Smith America Invents Act ( AIA", "United States Freedom Support Act" | |
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| other-livingthing | "insects", "monkeys", "patchouli" | |
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| other-medical | "Pediatrics", "amitriptyline", "pediatrician" | |
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| person-actor | "Ellaline Terriss", "Tchéky Karyo", "Edmund Payne" | |
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| person-artist/author | "George Axelrod", "Gaetano Donizett", "Hicks" | |
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| person-athlete | "Jaguar", "Neville", "Tozawa" | |
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| person-director | "Bob Swaim", "Richard Quine", "Frank Darabont" | |
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| person-other | "Richard Benson", "Holden", "Campbell" | |
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| person-politician | "William", "Rivière", "Emeric" | |
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| person-scholar | "Stedman", "Wurdack", "Stalmine" | |
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| person-soldier | "Helmuth Weidling", "Krukenberg", "Joachim Ziegler" | |
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| product-airplane | "Luton", "Spey-equipped FGR.2s", "EC135T2 CPDS" | |
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| product-car | "100EX", "Corvettes - GT1 C6R", "Phantom" | |
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| product-food | "red grape", "yakiniku", "V. labrusca" | |
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| product-game | "Airforce Delta", "Hardcore RPG", "Splinter Cell" | |
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| product-other | "Fairbottom Bobs", "X11", "PDP-1" | |
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| product-ship | "Congress", "Essex", "HMS `` Chinkara ''" | |
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| product-software | "AmiPDF", "Apdf", "Wikipedia" | |
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| product-train | "High Speed Trains", "55022", "Royal Scots Grey" | |
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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-bert-base-fewnerd-fine-super") |
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# Run inference |
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entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.") |
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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-bert-base-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-bert-base-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: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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 |