Update README.md

272d4e1 6 months ago

No virus

9.01 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:
	- tomaarsen/ner-orgs
	metrics:
	- precision
	- recall
	- f1
	widget:
	- text: The Fellowship of British Baptists and BMS World Mission brings together in
	ministry the churches that are members of the Baptist Union of Scotland, Wales,
	the Irish Baptist Networks, and the Baptist Union of Great Britain.
	- text: The program is classified in the National Collegiate Athletic Association
	(NCAA) Division I Bowl Subdivision (FBS), and the team competes in the Big 12
	Conference.
	- text: The Human Rights Foundation, condemned the assault, with HRF president Thor
	Halvorssen Mendoza claiming that "the PSUV approved of the attacks against opposition
	deputies at the National Assembly ".
	- text: But senior Conservatives, such as Commons Health Committee chairperson Sarah
	Wollaston and education minister Anne Milton, backed calls for a free vote on
	the issue, while Labour MP Stella Creasy said she would table an amendment on
	the matter to the Domestic Violence Bill and said that over 150 parliamentarians
	had expressed support for the change, and Labour's shadow Attorney General Shami
	Chakrabarti called the issue a test fo r May's feminism.
	- text: From 1991 to 1992, the Social Democratic Party and Social Democrats of Croatia
	were a part of the National Union government which was created by Franjo Tuđman
	during the first stages of the war.
	pipeline_tag: token-classification
	base_model: roberta-large
	model-index:
	- name: SpanMarker with roberta-large on FewNERD, CoNLL2003, and OntoNotes v5
	results:
	- task:
	type: token-classification
	name: Named Entity Recognition
	dataset:
	name: FewNERD, CoNLL2003, and OntoNotes v5
	type: tomaarsen/ner-orgs
	split: test
	metrics:
	- type: f1
	value: 0.81019
	name: F1
	- type: precision
	value: 0.8238
	name: Precision
	- type: recall
	value: 0.7970
	name: Recall
	---

	# SpanMarker with roberta-large on FewNERD, CoNLL2003, and OntoNotes v5

	This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [FewNERD, CoNLL2003, and OntoNotes v5](https://huggingface.co/datasets/tomaarsen/ner-orgs) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [roberta-large](https://huggingface.co/roberta-large) as the underlying encoder.

	## Model Details

	### Model Description
	- Model Type: SpanMarker
	- Encoder: [roberta-large](https://huggingface.co/roberta-large)
	- Maximum Sequence Length: 256 tokens
	- Maximum Entity Length: 8 words
	- Training Dataset: [FewNERD, CoNLL2003, and OntoNotes v5](https://huggingface.co/datasets/tomaarsen/ner-orgs)
	- 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 \|
	\|:------\|:---------------------------------------------\|
	\| ORG \| "IAEA", "Church 's Chicken", "Texas Chicken" \|

	## Evaluation

	### Metrics
	\| Label \| Precision \| Recall \| F1 \|
	\|:--------\|:----------\|:-------\|:-------\|
	\| ORG \| 0.8238 \| 0.7970 \| 0.81019\|

	## Uses

	### Direct Use for Inference

	```python
	from span_marker import SpanMarkerModel

	# Download from the 🤗 Hub
	model = SpanMarkerModel.from_pretrained("nbroad/span-marker-roberta-large-orgs-v1")
	# Run inference
	entities = model.predict("The program is classified in the National Collegiate Athletic Association (NCAA) Division I Bowl Subdivision (FBS), and the team competes in the Big 12 Conference.")
	```

	### 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("nbroad/span-marker-roberta-large-orgs-v1")

	# 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("nbroad/span-marker-roberta-large-orgs-v1-finetuned")
	```
	</details>

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	<!--
	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:----------------------\|:----\|:--------\|:----\|
	\| Sentence length \| 1 \| 23.5706 \| 263 \|
	\| Entities per sentence \| 0 \| 0.7865 \| 39 \|

	### Training Hyperparameters
	- learning_rate: 3e-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: 0.05
	- num_epochs: 3
	- mixed_precision_training: Native AMP

	### Training Results
	\| Epoch \| Step \| Validation Loss \| Validation Precision \| Validation Recall \| Validation F1 \| Validation Accuracy \|
	\|:------:\|:-----:\|:---------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-------------------:\|
	\| 0.1430 \| 600 \| 0.0085 \| 0.7425 \| 0.7383 \| 0.7404 \| 0.9726 \|
	\| 0.2860 \| 1200 \| 0.0078 \| 0.7503 \| 0.7516 \| 0.7510 \| 0.9741 \|
	\| 0.4290 \| 1800 \| 0.0077 \| 0.6962 \| 0.8107 \| 0.7491 \| 0.9718 \|
	\| 0.5720 \| 2400 \| 0.0060 \| 0.8074 \| 0.7486 \| 0.7769 \| 0.9753 \|
	\| 0.7150 \| 3000 \| 0.0057 \| 0.8135 \| 0.7717 \| 0.7921 \| 0.9770 \|
	\| 0.8580 \| 3600 \| 0.0059 \| 0.7997 \| 0.7764 \| 0.7879 \| 0.9763 \|
	\| 1.0010 \| 4200 \| 0.0057 \| 0.7860 \| 0.8051 \| 0.7954 \| 0.9771 \|
	\| 1.1439 \| 4800 \| 0.0058 \| 0.7907 \| 0.7717 \| 0.7811 \| 0.9763 \|
	\| 1.2869 \| 5400 \| 0.0058 \| 0.8116 \| 0.7803 \| 0.7956 \| 0.9774 \|
	\| 1.4299 \| 6000 \| 0.0056 \| 0.7918 \| 0.7850 \| 0.7884 \| 0.9770 \|
	\| 1.5729 \| 6600 \| 0.0056 \| 0.8097 \| 0.7837 \| 0.7965 \| 0.9769 \|
	\| 1.7159 \| 7200 \| 0.0055 \| 0.8113 \| 0.7790 \| 0.7948 \| 0.9765 \|
	\| 1.8589 \| 7800 \| 0.0052 \| 0.8095 \| 0.7970 \| 0.8032 \| 0.9782 \|
	\| 2.0019 \| 8400 \| 0.0054 \| 0.8244 \| 0.7782 \| 0.8006 \| 0.9774 \|
	\| 2.1449 \| 9000 \| 0.0053 \| 0.8238 \| 0.7970 \| 0.8102 \| 0.9782 \|
	\| 2.2879 \| 9600 \| 0.0053 \| 0.82 \| 0.7901 \| 0.8048 \| 0.9773 \|
	\| 2.4309 \| 10200 \| 0.0053 \| 0.8243 \| 0.7936 \| 0.8086 \| 0.9785 \|
	\| 2.5739 \| 10800 \| 0.0053 \| 0.8159 \| 0.7953 \| 0.8055 \| 0.9781 \|
	\| 2.7169 \| 11400 \| 0.0053 \| 0.8072 \| 0.8034 \| 0.8053 \| 0.9784 \|
	\| 2.8599 \| 12000 \| 0.0052 \| 0.8111 \| 0.8017 \| 0.8064 \| 0.9782 \|

	### Framework Versions
	- Python: 3.10.12
	- SpanMarker: 1.5.0
	- Transformers: 4.35.2
	- PyTorch: 2.1.0a0+32f93b1
	- Datasets: 2.15.0
	- Tokenizers: 0.15.0

	## Citation

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

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->