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	---
	language:
	- en
	license: cc-by-4.0
	library_name: span-marker
	tags:
	- span-marker
	- token-classification
	- ner
	- named-entity-recognition
	- generated_from_span_marker_trainer
	datasets:
	- EMBO/SourceData
	metrics:
	- precision
	- recall
	- f1
	widget:
	- text: Comparison of ENCC-derived neurospheres treated with intestinal extract
	from hypoganglionosis rats, hypoganglionosis treated with Fecal microbiota transplantation
	(FMT) sham rat. Comparison of neuronal markers. (J) Immunofluorescence stain
	number of PGP9.5+. Nuclei were stained blue with DAPI; Triangles indicate
	PGP9.5+.
	- text: 'Histochemical (H & E) immunostaining (red) show T (CD3+) neutrophil
	(Ly6b+) infiltration in skin of mice in (A). Scale bar, 100 μm. (of CD3
	Ly6b immunostaining from CsA treated mice represent seperate analyses performed
	on serial thin sections.) of epidermal thickness, T (CD3+) neutrophil (Ly6b+)
	infiltration (red) in skin thin sections from (C), (n = 6). Data
	information: Data represent mean ± SD. * P < 0.05, * * P < 0.01 by two
	-Mann-Whitney; two independent experiments.'
	- text: 'C African green monkey kidney epithelial (Vero) were transfected with NC,
	siMLKL, or miR-324-5p for 48 h. qPCR for expression of MLKL. Data information:
	data are represented as means ± SD of three biological replicates. Statistical
	analyses were performed using unpaired Student '' s t -. experiments were performed
	at least three times, representative data are shown.'
	- text: (F) Binding between FTCD p47 between p47 p97 is necessary for mitochondria
	aggregation mediated by FTCDwt-HA-MAO. HeLa Tet-off inducibly expressing
	FTCDwt-HA-MAO were transfected with mammalian expression constructs of
	siRNA-insensitive Flag-tagged p47wt / mutants at same time as treatment of p47
	siRNA, cultured for 24 hrs. were further cultured in DOX-free medium for 48 hrs
	for induction of FTCD-HA-MAO. After fixation, were visualized with a monoclonal
	antibody to mitochondria polyclonal antibodies to HA Flag. Panels a-l display
	representative. Scale bar = 10 μm. (G) Binding between FTCD p97 is necessary
	for mitochondria aggregation mediated by FTCDwt-HA-MAO. HeLa Tet-off inducibly
	expressing FTCDwt-HA-MAO were transfected with mammalian expression construct
	of siRNA-insensitive Flag-tagged p97wt / mutant at same time as treatment
	with p97 siRNA. following procedures were same as in (F). Panels a-i display
	representative. Scale bar = 10 μm. (H) results of of (F) (G). Results
	are shown as mean ± SD of five sets of independent experiments, with 100 counted
	in each group in each independent experiment. Asterisks indicate a significant
	difference at P < 0.01 compared with siRNA treatment alone ('none') compared
	with mutant expression (Bonferroni method).
	- text: (b) Parkin is recruited selectively to depolarized mitochondria directs
	mitophagy. HeLa transfected with HA-Parkin were treated with CCCP for indicated
	times. Mitochondria were stained by anti-TOM20 (pseudo coloured; blue) a
	ΔΨm dependent MitoTracker (red). Parkin was stained with anti-HA (green).
	Without treatment, mitochondria are intact stained by both mitochondrial
	markers, whereas Parkin is equally distributed in cytoplasm. After 2 h of CCCP
	treatment, mitochondria are depolarized as shown by loss of MitoTracker. Parkin
	completely translocates to mitochondria clustering at perinuclear regions. After
	24h of CCCP treatment, massive loss of mitochondria is observed as shown by
	disappearance of mitochondrial marker. Only Parkin-positive show mitochondrial
	clustering clearance, in contrast to adjacent untransfected. Scale bars, 10
	μm.
	pipeline_tag: token-classification
	base_model: bert-base-uncased
	model-index:
	- name: SpanMarker with bert-base-uncased on SourceData
	results:
	- task:
	type: token-classification
	name: Named Entity Recognition
	dataset:
	name: SourceData
	type: EMBO/SourceData
	split: test
	metrics:
	- type: f1
	value: 0.8336481983993405
	name: F1
	- type: precision
	value: 0.8345368269032392
	name: Precision
	- type: recall
	value: 0.8327614603348888
	name: Recall
	---

	# SpanMarker with bert-base-uncased on SourceData

	This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [SourceData](https://huggingface.co/datasets/EMBO/SourceData) 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.

	## 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: [SourceData](https://huggingface.co/datasets/EMBO/SourceData)
	- Language: en
	- License: cc-by-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 \|
	\|:---------------\|:--------------------------------------------------------\|
	\| CELL_LINE \| "293T", "WM266.4 451Lu", "501mel" \|
	\| CELL_TYPE \| "BMDMs", "protoplasts", "epithelial" \|
	\| DISEASE \| "melanoma", "lung metastasis", "breast prostate cancer" \|
	\| EXP_ASSAY \| "interactions", "Yeast two-hybrid", "BiFC" \|
	\| GENEPROD \| "CPL1", "FREE1 CPL1", "FREE1" \|
	\| ORGANISM \| "Arabidopsis", "yeast", "seedlings" \|
	\| SMALL_MOLECULE \| "polyacrylamide", "CHX", "SDS polyacrylamide" \|
	\| SUBCELLULAR \| "proteasome", "D-bodies", "plasma" \|
	\| TISSUE \| "Colon", "roots", "serum" \|

	## Evaluation

	### Metrics
	\| Label \| Precision \| Recall \| F1 \|
	\|:---------------\|:----------\|:-------\|:-------\|
	\| all \| 0.8345 \| 0.8328 \| 0.8336 \|
	\| CELL_LINE \| 0.9060 \| 0.8866 \| 0.8962 \|
	\| CELL_TYPE \| 0.7365 \| 0.7746 \| 0.7551 \|
	\| DISEASE \| 0.6204 \| 0.6531 \| 0.6363 \|
	\| EXP_ASSAY \| 0.7224 \| 0.7096 \| 0.7160 \|
	\| GENEPROD \| 0.8944 \| 0.8960 \| 0.8952 \|
	\| ORGANISM \| 0.8752 \| 0.8902 \| 0.8826 \|
	\| SMALL_MOLECULE \| 0.8304 \| 0.8223 \| 0.8263 \|
	\| SUBCELLULAR \| 0.7859 \| 0.7699 \| 0.7778 \|
	\| TISSUE \| 0.8134 \| 0.8056 \| 0.8094 \|

	## 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-sourcedata")
	# Run inference
	entities = model.predict("Comparison of ENCC-derived neurospheres treated with intestinal extract from hypoganglionosis rats, hypoganglionosis treated with Fecal microbiota transplantation (FMT) sham rat. Comparison of neuronal markers. (J) Immunofluorescence stain number of PGP9.5+. Nuclei were stained blue with DAPI; Triangles indicate PGP9.5+.")
	```

	### 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-sourcedata")

	# 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-sourcedata-finetuned")
	```
	</details>

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	## Training Details

	### Training Set Metrics
	\| Training set \| Min \| Median \| Max \|
	\|:----------------------\|:----\|:--------\|:-----\|
	\| Sentence length \| 4 \| 71.0253 \| 2609 \|
	\| Entities per sentence \| 0 \| 8.3186 \| 162 \|

	### 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: 3

	### Training Results
	\| Epoch \| Step \| Validation Loss \| Validation Precision \| Validation Recall \| Validation F1 \| Validation Accuracy \|
	\|:------:\|:-----:\|:---------------:\|:--------------------:\|:-----------------:\|:-------------:\|:-------------------:\|
	\| 0.5237 \| 3000 \| 0.0162 \| 0.7972 \| 0.8162 \| 0.8065 \| 0.9520 \|
	\| 1.0473 \| 6000 \| 0.0155 \| 0.8188 \| 0.8251 \| 0.8219 \| 0.9560 \|
	\| 1.5710 \| 9000 \| 0.0155 \| 0.8213 \| 0.8324 \| 0.8268 \| 0.9563 \|
	\| 2.0946 \| 12000 \| 0.0163 \| 0.8315 \| 0.8347 \| 0.8331 \| 0.9581 \|
	\| 2.6183 \| 15000 \| 0.0167 \| 0.8303 \| 0.8378 \| 0.8340 \| 0.9582 \|

	### Framework Versions

	- Python: 3.9.16
	- SpanMarker: 1.3.1.dev
	- Transformers: 4.33.0
	- 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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