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metadata
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 model trained on the SourceData dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-uncased as the underlying encoder.

Model Details

Model Description

  • Model Type: SpanMarker
  • Encoder: bert-base-uncased
  • Maximum Sequence Length: 256 tokens
  • Maximum Entity Length: 8 words
  • Training Dataset: SourceData
  • Language: en
  • License: cc-by-4.0

Model Sources

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

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.

Click to expand
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")

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}
}