Fix base-encoder link in HF hub

e069e5c about 1 year ago

17.1 kB

	---
	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:
	- precision
	- recall
	- f1
	widget:
	- 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.
	- text: 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.
	- 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.'
	- 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.
	- 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.
	pipeline_tag: token-classification
	base_model: numind/generic-entity_recognition_NER-v1
	model-index:
	- name: SpanMarker with numind/generic-entity_recognition-v1 on FewNERD
	results:
	- task:
	type: token-classification
	name: Named Entity Recognition
	dataset:
	name: FewNERD
	type: DFKI-SLT/few-nerd
	split: eval
	metrics:
	- type: f1
	value: 0.7039859923782059
	name: F1
	- type: precision
	value: 0.7047408904377952
	name: Precision
	- type: recall
	value: 0.7032327098380559
	name: Recall
	---

	# SpanMarker with numind/generic-entity_recognition-v1 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 [numind/generic-entity_recognition_NER-v1](https://huggingface.co/numind/generic-entity_recognition_NER-v1) as the underlying encoder.

	## Model Details

	### Model Description
	- Model Type: SpanMarker
	- Encoder: [numind/generic-entity_recognition_NER-v1](https://huggingface.co/numind/generic-entity_recognition_NER-v1)
	- 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 \| "Corazones", "The Gale Storm Show : Oh , Susanna", "Street Cents" \|
	\| art-film \| "Shawshank Redemption", "L'Atlantide", "Bosch" \|
	\| art-music \| "Hollywood Studio Symphony", "Atkinson , Danko and Ford ( with Brockie and Hilton )", "Champion Lover" \|
	\| art-other \| "The Today Show", "Venus de Milo", "Aphrodite of Milos" \|
	\| art-painting \| "Production/Reproduction", "Touit", "Cofiwch Dryweryn" \|
	\| art-writtenart \| "The Seven Year Itch", "Imelda de ' Lambertazzi", "Time" \|
	\| building-airport \| "Sheremetyevo International Airport", "Newark Liberty International Airport", "Luton Airport" \|
	\| building-hospital \| "Yeungnam University Hospital", "Hokkaido University Hospital", "Memorial Sloan-Kettering Cancer Center" \|
	\| building-hotel \| "The Standard Hotel", "Flamingo Hotel", "Radisson Blu Sea Plaza Hotel" \|
	\| building-library \| "British Library", "Bayerische Staatsbibliothek", "Berlin State Library" \|
	\| building-other \| "Henry Ford Museum", "Alpha Recording Studios", "Communiplex" \|
	\| building-restaurant \| "Carnegie Deli", "Fatburger", "Trumbull" \|
	\| building-sportsfacility \| "Boston Garden", "Sports Center", "Glenn Warner Soccer Facility" \|
	\| building-theater \| "Sanders Theatre", "National Paris Opera", "Pittsburgh Civic Light Opera" \|
	\| event-attack/battle/war/militaryconflict \| "Easter Offensive", "Jurist", "Vietnam War" \|
	\| event-disaster \| "the 1912 North Mount Lyell Disaster", "1990s North Korean famine", "1693 Sicily earthquake" \|
	\| event-election \| "Elections to the European Parliament", "March 1898 elections", "1982 Mitcham and Morden by-election" \|
	\| event-other \| "Union for a Popular Movement", "Masaryk Democratic Movement", "Eastwood Scoring Stage" \|
	\| event-protest \| "Iranian Constitutional Revolution", "French Revolution", "Russian Revolution" \|
	\| event-sportsevent \| "World Cup", "National Champions", "Stanley Cup" \|
	\| location-GPE \| "Croatian", "Mediterranean Basin", "the Republic of Croatia" \|
	\| location-bodiesofwater \| "Arthur Kill", "Atatürk Dam Lake", "Norfolk coast" \|
	\| location-island \| "new Samsat district", "Laccadives", "Staten Island" \|
	\| location-mountain \| "Salamander Glacier", "Miteirya Ridge", "Ruweisat Ridge" \|
	\| location-other \| "Victoria line", "Northern City Line", "Cartuther" \|
	\| location-park \| "Painted Desert Community Complex Historic District", "Gramercy Park", "Shenandoah National Park" \|
	\| location-road/railway/highway/transit \| "NJT", "Newark-Elizabeth Rail Link", "Friern Barnet Road" \|
	\| organization-company \| "Texas Chicken", "Dixy 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 \| "Clash", "Al Jazeera", "TimeOut Melbourne" \|
	\| organization-other \| "Defence Sector C", "IAEA", "4th Army" \|
	\| organization-politicalparty \| "Al Wafa ' Islamic", "Shimpotō", "Kenseitō" \|
	\| organization-religion \| "UPCUSA", "Christian", "Jewish" \|
	\| organization-showorganization \| "Lizzy", "Bochumer Symphoniker", "Mr. Mister" \|
	\| organization-sportsleague \| "China League One", "NHL", "First Division" \|
	\| organization-sportsteam \| "Arsenal", "Luc Alphand Aventures", "Tottenham" \|
	\| other-astronomything \| "Algol", "`` Caput Larvae ''", "Zodiac" \|
	\| other-award \| "Order of the Republic of Guinea and Nigeria", "Grand Commander of the Order of the Niger", "GCON" \|
	\| other-biologything \| "N-terminal lipid", "Amphiphysin", "BAR" \|
	\| other-chemicalthing \| "uranium", "carbon dioxide", "sulfur" \|
	\| other-currency \| "$", "lac crore", "Travancore Rupee" \|
	\| other-disease \| "bladder cancer", "French Dysentery Epidemic of 1779", "hypothyroidism" \|
	\| other-educationaldegree \| "BSc ( Hons ) in physics", "Bachelor", "Master" \|
	\| other-god \| "Raijin", "Fujin", "El" \|
	\| other-language \| "Breton-speaking", "Latin", "English" \|
	\| other-law \| "Leahy–Smith America Invents Act ( AIA", "United States Freedom Support Act", "Thirty Years ' Peace" \|
	\| other-livingthing \| "monkeys", "patchouli", "insects" \|
	\| other-medical \| "amitriptyline", "Pediatrics", "pediatrician" \|
	\| person-actor \| "Tchéky Karyo", "Edmund Payne", "Ellaline Terriss" \|
	\| person-artist/author \| "Hicks", "Gaetano Donizett", "George Axelrod" \|
	\| person-athlete \| "Tozawa", "Neville", "Jaguar" \|
	\| person-director \| "Richard Quine", "Bob Swaim", "Frank Darabont" \|
	\| person-other \| "Campbell", "Holden", "Richard Benson" \|
	\| person-politician \| "William", "Rivière", "Emeric" \|
	\| person-scholar \| "Wurdack", "Stalmine", "Stedman" \|
	\| person-soldier \| "Joachim Ziegler", "Helmuth Weidling", "Krukenberg" \|
	\| product-airplane \| "Spey-equipped FGR.2s", "EC135T2 CPDS", "Luton" \|
	\| product-car \| "Phantom", "100EX", "Corvettes - GT1 C6R" \|
	\| product-food \| "red grape", "yakiniku", "V. labrusca" \|
	\| product-game \| "Hardcore RPG", "Splinter Cell", "Airforce Delta" \|
	\| product-other \| "X11", "PDP-1", "Fairbottom Bobs" \|
	\| product-ship \| "Essex", "Congress", "HMS `` Chinkara ''" \|
	\| product-software \| "AmiPDF", "Wikipedia", "Apdf" \|
	\| product-train \| "55022", "Royal Scots Grey", "High Speed Trains" \|
	\| product-weapon \| "AR-15 's", "ZU-23-2MR Wróbel II", "ZU-23-2M Wróbel" \|

	## Uses

	### Direct Use for Inference

	```python
	from span_marker import SpanMarkerModel

	# Download from the 🤗 Hub
	model = SpanMarkerModel.from_pretrained("guishe/span-marker-generic-entity_recognition-v1-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("guishe/span-marker-generic-entity_recognition-v1-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("guishe/span-marker-generic-entity_recognition-v1-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: 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 \|
	\|:------:\|:-----:\|:---------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-------------------:\|
	\| 0.2980 \| 3000 \| 0.0290 \| 0.6503 \| 0.6402 \| 0.6452 \| 0.9109 \|
	\| 0.5961 \| 6000 \| 0.0250 \| 0.6749 \| 0.6794 \| 0.6772 \| 0.9202 \|
	\| 0.8941 \| 9000 \| 0.0236 \| 0.6908 \| 0.6871 \| 0.6889 \| 0.9229 \|
	\| 1.1921 \| 12000 \| 0.0234 \| 0.6853 \| 0.7007 \| 0.6929 \| 0.9239 \|
	\| 1.4902 \| 15000 \| 0.0227 \| 0.6966 \| 0.6929 \| 0.6948 \| 0.9241 \|
	\| 1.7882 \| 18000 \| 0.0221 \| 0.7073 \| 0.6922 \| 0.6997 \| 0.9250 \|
	\| 2.0862 \| 21000 \| 0.0223 \| 0.7003 \| 0.6993 \| 0.6998 \| 0.9252 \|
	\| 2.3843 \| 24000 \| 0.0222 \| 0.6971 \| 0.7027 \| 0.6999 \| 0.9254 \|
	\| 2.6823 \| 27000 \| 0.0219 \| 0.7044 \| 0.7004 \| 0.7024 \| 0.9259 \|
	\| 2.9803 \| 30000 \| 0.0219 \| 0.7047 \| 0.7032 \| 0.7040 \| 0.9261 \|

	### Framework Versions
	- Python: 3.10.8
	- SpanMarker: 1.5.0
	- Transformers: 4.28.0
	- PyTorch: 1.13.1+cu117
	- Datasets: 2.14.4
	- Tokenizers: 0.13.3

	## Citation

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