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	---
	language:
	- en
	license: apache-2.0
	library_name: span-marker
	tags:
	- span-marker
	- token-classification
	- ner
	- named-entity-recognition
	- generated_from_span_marker_trainer
	datasets:
	- acronym_identification
	metrics:
	- precision
	- recall
	- f1
	widget:
	- text: "here, da = direct assessment, rr = relative ranking, ds = discrete scale and cs = continuous scale."
	example_title: "Uncased 1"
	- text: "modifying or replacing the erasable programmable read only memory (eprom) in a phone would allow the configuration of any esn and min via software for cellular devices."
	example_title: "Uncased 2"
	- text: "we propose a technique called aggressive stochastic weight averaging (aswa) and an extension called norm-filtered aggressive stochastic weight averaging (naswa) which improves te stability of models over random seeds."
	example_title: "Uncased 3"
	- text: "the choice of the encoder and decoder modules of dnpg can be quite flexible, for instance long-short term memory networks (lstm) or convolutional neural network (cnn)."
	example_title: "Uncased 4"
	pipeline_tag: token-classification
	co2_eq_emissions:
	emissions: 31.203903222402037
	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: 0.272
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	base_model: bert-base-uncased
	model-index:
	- name: SpanMarker with bert-base-uncased on Acronym Identification
	results:
	- task:
	type: token-classification
	name: Named Entity Recognition
	dataset:
	name: Acronym Identification
	type: acronym_identification
	split: validation
	metrics:
	- type: f1
	value: 0.9198933333333332
	name: F1
	- type: precision
	value: 0.9339397877409573
	name: Precision
	- type: recall
	value: 0.9062631357713324
	name: Recall
	---

	# SpanMarker with bert-base-uncased on Acronym Identification

	This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [Acronym Identification](https://huggingface.co/datasets/acronym_identification) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the underlying encoder. See [train.py](train.py) for the training script.

	Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance: [tomaarsen/span-marker-bert-base-acronyms](https://huggingface.co/tomaarsen/span-marker-bert-base-acronyms).

	## Model Details

	### Model Description

	- Model Type: SpanMarker
	- Encoder: [bert-base-uncased](https://huggingface.co/bert-base-uncased)
	- Maximum Sequence Length: 256 tokens
	- Maximum Entity Length: 8 words
	- Training Dataset: [Acronym Identification](https://huggingface.co/datasets/acronym_identification)
	- Language: en
	- License: apache-2.0

	### 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 \|
	\|:------\|:------------------------------------------------------------------------------------------------------\|
	\| long \| "successive convex approximation", "controlled natural language", "Conversational Question Answering" \|
	\| short \| "SODA", "CNL", "CoQA" \|

	## Evaluation

	### Metrics
	\| Label \| Precision \| Recall \| F1 \|
	\|:--------\|:----------\|:-------\|:-------\|
	\| all \| 0.9339 \| 0.9063 \| 0.9199 \|
	\| long \| 0.9314 \| 0.8845 \| 0.9074 \|
	\| short \| 0.9352 \| 0.9174 \| 0.9262 \|

	## Uses

	### Direct Use for Inference

	```python
	from span_marker import SpanMarkerModel

	# Download from the 🤗 Hub
	model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-acronyms")
	# Run inference
	entities = model.predict("compression algorithms like principal component analysis (pca) can reduce noise and complexity.")
	```

	### 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-bert-base-uncased-acronyms")

	# 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-uncased-acronyms-finetuned")
	```
	</details>

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:----------------------\|:----\|:--------\|:----\|
	\| Sentence length \| 4 \| 32.3372 \| 170 \|
	\| Entities per sentence \| 0 \| 2.6775 \| 24 \|

	### 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: 2

	### Training Results
	\| Epoch \| Step \| Validation Loss \| Validation Precision \| Validation Recall \| Validation F1 \| Validation Accuracy \|
	\|:------:\|:----:\|:---------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-------------------:\|
	\| 0.3120 \| 200 \| 0.0097 \| 0.8999 \| 0.8731 \| 0.8863 \| 0.9718 \|
	\| 0.6240 \| 400 \| 0.0075 \| 0.9163 \| 0.8995 \| 0.9078 \| 0.9769 \|
	\| 0.9360 \| 600 \| 0.0076 \| 0.9079 \| 0.9153 \| 0.9116 \| 0.9773 \|
	\| 1.2480 \| 800 \| 0.0069 \| 0.9267 \| 0.9006 \| 0.9135 \| 0.9778 \|
	\| 1.5601 \| 1000 \| 0.0065 \| 0.9268 \| 0.9044 \| 0.9154 \| 0.9782 \|
	\| 1.8721 \| 1200 \| 0.0065 \| 0.9279 \| 0.9061 \| 0.9168 \| 0.9787 \|

	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Carbon Emitted: 0.031 kg of CO2
	- Hours Used: 0.272 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: 1.3.1.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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