Update widget examples with multilingual sentences

4dde3a0 8 months ago

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	---
	language:
	- en
	- multilingual
	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:
	- precision
	- recall
	- f1
	widget:
	- text: "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris."
	example_title: "English 1"
	- text: The WPC led the international peace movement in the decade after the Second
	World War, but its failure to speak out against the Soviet suppression of the
	1956 Hungarian uprising and the resumption of Soviet nuclear tests in 1961 marginalised
	it, and in the 1960s it was eclipsed by the newer, non-aligned peace organizations
	like the Campaign for Nuclear Disarmament.
	example_title: "English 2"
	- text: Most of the Steven Seagal movie "Under Siege" (co-starring Tommy Lee Jones)
	was filmed on the Battleship USS Alabama, which is docked on Mobile Bay at Battleship
	Memorial Park and open to the public.
	example_title: "English 3"
	- text: 'The Central African CFA franc (French: "franc CFA" or simply "franc", ISO
	4217 code: XAF) is the currency of six independent states in Central Africa: Cameroon,
	Central African Republic, Chad, Republic of the Congo, Equatorial Guinea and Gabon.'
	example_title: "English 4"
	- text: Brenner conducted post-doctoral research at Brandeis University with Gregory
	Petsko and then took his first academic position at Thomas Jefferson University
	in 1996, moving to Dartmouth Medical School in 2003, where he served as Associate
	Director for Basic Sciences at Norris Cotton Cancer Center.
	example_title: "English 5"
	- text: On Friday, October 27, 2017, the Senate of Spain (Senado) voted 214 to 47
	to invoke Article 155 of the Spanish Constitution over Catalonia after the Catalan
	Parliament declared the independence.
	example_title: "English 6"
	- text: "Amelia Earthart voló su Lockheed Vega 5B monomotor a través del Océano Atlántico hasta París."
	example_title: "Spanish"
	- text: "Amelia Earthart a fait voler son monomoteur Lockheed Vega 5B à travers l'ocean Atlantique jusqu'à Paris."
	example_title: "French"
	- text: "Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B über den Atlantik nach Paris."
	example_title: "German"
	- text: "Амелия Эртхарт перелетела на своем одномоторном самолете Lockheed Vega 5B через Атлантический океан в Париж."
	example_title: "Russian"
	- text: "Amelia Earthart vloog met haar één-motorige Lockheed Vega 5B over de Atlantische Oceaan naar Parijs."
	example_title: "Dutch"
	- text: "Amelia Earthart przeleciała swoim jednosilnikowym samolotem Lockheed Vega 5B przez Ocean Atlantycki do Paryża."
	example_title: "Polish"
	- text: "Amelia Earthart flaug eins hreyfils Lockheed Vega 5B yfir Atlantshafið til Parísar."
	example_title: "Icelandic"
	- text: "Η Amelia Earthart πέταξε το μονοκινητήριο Lockheed Vega 5B της πέρα από τον Ατλαντικό Ωκεανό στο Παρίσι."
	example_title: "Greek"
	pipeline_tag: token-classification
	co2_eq_emissions:
	emissions: 452.84872035276965
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
	ram_total_size: 31.777088165283203
	hours_used: 3.118
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	base_model: xlm-roberta-base
	model-index:
	- name: SpanMarker with xlm-roberta-base on FewNERD
	results:
	- task:
	type: token-classification
	name: Named Entity Recognition
	dataset:
	name: FewNERD
	type: DFKI-SLT/few-nerd
	split: test
	metrics:
	- type: f1
	value: 0.6884821229658107
	name: F1
	- type: precision
	value: 0.6890426017339362
	name: Precision
	- type: recall
	value: 0.6879225552622042
	name: Recall
	---

	# SpanMarker with xlm-roberta-base 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 [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder.

	## Model Details

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

	## Evaluation

	### Metrics
	\| Label \| Precision \| Recall \| F1 \|
	\|:-----------------------------------------\|:----------\|:-------\|:-------\|
	\| all \| 0.6890 \| 0.6879 \| 0.6885 \|
	\| art-broadcastprogram \| 0.6 \| 0.5771 \| 0.5883 \|
	\| art-film \| 0.7384 \| 0.7453 \| 0.7419 \|
	\| art-music \| 0.7930 \| 0.7221 \| 0.7558 \|
	\| art-other \| 0.4245 \| 0.2900 \| 0.3446 \|
	\| art-painting \| 0.5476 \| 0.4035 \| 0.4646 \|
	\| art-writtenart \| 0.6400 \| 0.6539 \| 0.6469 \|
	\| building-airport \| 0.8219 \| 0.8242 \| 0.8230 \|
	\| building-hospital \| 0.7024 \| 0.8104 \| 0.7526 \|
	\| building-hotel \| 0.7175 \| 0.7283 \| 0.7228 \|
	\| building-library \| 0.74 \| 0.7296 \| 0.7348 \|
	\| building-other \| 0.5828 \| 0.5910 \| 0.5869 \|
	\| building-restaurant \| 0.5525 \| 0.5216 \| 0.5366 \|
	\| building-sportsfacility \| 0.6187 \| 0.7881 \| 0.6932 \|
	\| building-theater \| 0.7067 \| 0.7626 \| 0.7336 \|
	\| event-attack/battle/war/militaryconflict \| 0.7544 \| 0.7468 \| 0.7506 \|
	\| event-disaster \| 0.5882 \| 0.5314 \| 0.5584 \|
	\| event-election \| 0.4167 \| 0.2198 \| 0.2878 \|
	\| event-other \| 0.4902 \| 0.4042 \| 0.4430 \|
	\| event-protest \| 0.3643 \| 0.2831 \| 0.3186 \|
	\| event-sportsevent \| 0.6125 \| 0.6239 \| 0.6182 \|
	\| location-GPE \| 0.8102 \| 0.8553 \| 0.8321 \|
	\| location-bodiesofwater \| 0.6888 \| 0.7725 \| 0.7282 \|
	\| location-island \| 0.7285 \| 0.6440 \| 0.6836 \|
	\| location-mountain \| 0.7129 \| 0.7327 \| 0.7227 \|
	\| location-other \| 0.4376 \| 0.2560 \| 0.3231 \|
	\| location-park \| 0.6991 \| 0.6900 \| 0.6945 \|
	\| location-road/railway/highway/transit \| 0.6936 \| 0.7259 \| 0.7094 \|
	\| organization-company \| 0.6921 \| 0.6912 \| 0.6917 \|
	\| organization-education \| 0.7838 \| 0.7963 \| 0.7900 \|
	\| organization-government/governmentagency \| 0.5363 \| 0.4394 \| 0.4831 \|
	\| organization-media/newspaper \| 0.6215 \| 0.6705 \| 0.6451 \|
	\| organization-other \| 0.5766 \| 0.5157 \| 0.5444 \|
	\| organization-politicalparty \| 0.6449 \| 0.7324 \| 0.6859 \|
	\| organization-religion \| 0.5139 \| 0.6057 \| 0.5560 \|
	\| organization-showorganization \| 0.5620 \| 0.5657 \| 0.5638 \|
	\| organization-sportsleague \| 0.6348 \| 0.6542 \| 0.6443 \|
	\| organization-sportsteam \| 0.7138 \| 0.7566 \| 0.7346 \|
	\| other-astronomything \| 0.7418 \| 0.7625 \| 0.752 \|
	\| other-award \| 0.7291 \| 0.6736 \| 0.7002 \|
	\| other-biologything \| 0.6735 \| 0.6275 \| 0.6497 \|
	\| other-chemicalthing \| 0.6025 \| 0.5651 \| 0.5832 \|
	\| other-currency \| 0.6843 \| 0.8411 \| 0.7546 \|
	\| other-disease \| 0.6284 \| 0.7089 \| 0.6662 \|
	\| other-educationaldegree \| 0.5856 \| 0.6033 \| 0.5943 \|
	\| other-god \| 0.6089 \| 0.6913 \| 0.6475 \|
	\| other-language \| 0.6608 \| 0.7968 \| 0.7225 \|
	\| other-law \| 0.6693 \| 0.7246 \| 0.6958 \|
	\| other-livingthing \| 0.6070 \| 0.6014 \| 0.6042 \|
	\| other-medical \| 0.5062 \| 0.5113 \| 0.5088 \|
	\| person-actor \| 0.8274 \| 0.7673 \| 0.7962 \|
	\| person-artist/author \| 0.6761 \| 0.7294 \| 0.7018 \|
	\| person-athlete \| 0.8132 \| 0.8347 \| 0.8238 \|
	\| person-director \| 0.675 \| 0.6823 \| 0.6786 \|
	\| person-other \| 0.6472 \| 0.6388 \| 0.6429 \|
	\| person-politician \| 0.6621 \| 0.6593 \| 0.6607 \|
	\| person-scholar \| 0.5181 \| 0.5007 \| 0.5092 \|
	\| person-soldier \| 0.4750 \| 0.5131 \| 0.4933 \|
	\| product-airplane \| 0.6230 \| 0.6717 \| 0.6464 \|
	\| product-car \| 0.7293 \| 0.7176 \| 0.7234 \|
	\| product-food \| 0.5758 \| 0.5185 \| 0.5457 \|
	\| product-game \| 0.7049 \| 0.6734 \| 0.6888 \|
	\| product-other \| 0.5477 \| 0.4067 \| 0.4668 \|
	\| product-ship \| 0.6247 \| 0.6395 \| 0.6320 \|
	\| product-software \| 0.6497 \| 0.6760 \| 0.6626 \|
	\| product-train \| 0.5505 \| 0.5732 \| 0.5616 \|
	\| product-weapon \| 0.6004 \| 0.4744 \| 0.5300 \|

	## Uses

	### Direct Use for Inference

	```python
	from span_marker import SpanMarkerModel

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

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	## 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: 16
	- eval_batch_size: 16
	- 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 Results
	\| Epoch \| Step \| Validation Loss \| Validation Precision \| Validation Recall \| Validation F1 \| Validation Accuracy \|
	\|:------:\|:-----:\|:---------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-------------------:\|
	\| 0.2947 \| 3000 \| 0.0318 \| 0.6058 \| 0.5990 \| 0.6024 \| 0.9020 \|
	\| 0.5893 \| 6000 \| 0.0266 \| 0.6556 \| 0.6679 \| 0.6617 \| 0.9173 \|
	\| 0.8840 \| 9000 \| 0.0250 \| 0.6691 \| 0.6804 \| 0.6747 \| 0.9206 \|
	\| 1.1787 \| 12000 \| 0.0239 \| 0.6865 \| 0.6761 \| 0.6813 \| 0.9212 \|
	\| 1.4733 \| 15000 \| 0.0234 \| 0.6872 \| 0.6812 \| 0.6842 \| 0.9226 \|
	\| 1.7680 \| 18000 \| 0.0231 \| 0.6919 \| 0.6821 \| 0.6870 \| 0.9227 \|
	\| 2.0627 \| 21000 \| 0.0231 \| 0.6909 \| 0.6871 \| 0.6890 \| 0.9233 \|
	\| 2.3573 \| 24000 \| 0.0231 \| 0.6903 \| 0.6875 \| 0.6889 \| 0.9238 \|
	\| 2.6520 \| 27000 \| 0.0229 \| 0.6918 \| 0.6926 \| 0.6922 \| 0.9242 \|
	\| 2.9467 \| 30000 \| 0.0228 \| 0.6927 \| 0.6930 \| 0.6928 \| 0.9243 \|

	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Carbon Emitted: 0.453 kg of CO2
	- Hours Used: 3.118 hours

	### 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: 1.4.1.dev
	- Transformers: 4.30.0
	- PyTorch: 2.0.1+cu118
	- Datasets: 2.14.0
	- Tokenizers: 0.13.2

	## Citation

	### BibTeX
	```
	@software{Aarsen_SpanMarker,
	author = {Aarsen, Tom},
	license = {Apache-2.0},
	title = {{SpanMarker for Named Entity Recognition}},
	url = {https://github.com/tomaarsen/SpanMarkerNER}
	}
	```

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