VOOZH about

URL: https://huggingface.co/tomaarsen/span-marker-bert-base-uncased-sourcedata

⇱ tomaarsen/span-marker-bert-base-uncased-sourcedata · Hugging Face

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.

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}
}
Downloads last month
3

Model tree for tomaarsen/span-marker-bert-base-uncased-sourcedata

Finetuned
(6775)
this model

Dataset used to train tomaarsen/span-marker-bert-base-uncased-sourcedata

Collection including tomaarsen/span-marker-bert-base-uncased-sourcedata

Evaluation results