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span-marker-bert-base-uncased-bionlp

Token Classification
SpanMarker
PyTorch
TensorBoard
English
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results
Carbon Emissions
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metadata
language: en
license: other
library_name: span-marker
tags:
  - span-marker
  - token-classification
  - ner
  - named-entity-recognition
  - generated_from_span_marker_trainer
datasets:
  - tner/bionlp2004
metrics:
  - precision
  - recall
  - f1
widget:
  - text: >-
      Coexpression of HMG I/Y and Oct-2 in cell lines lacking Oct-2 results in
      high levels of HLA-DRA gene expression, and in vitro DNA-binding studies
      reveal that HMG I/Y stimulates Oct-2A binding to the HLA-DRA promoter.
  - text: >-
      In erythroid cells most of the transcription activity was contained in a
      150 bp promoter fragment with binding sites for transcription factors AP2,
      Sp1 and the erythroid-specific GATA-1.
  - text: >-
      Synergy between signal transduction pathways is obligatory for expression
      of c-fos in B and T cell lines: implication for c-fos control via surface
      immunoglobulin and T cell antigen receptors.
  - text: >-
      CIITA mRNA is normally inducible by IFN-gamma in class II non-inducible,
      RB-defective lines, and in one line, re-expression of RB has no effect on
      CIITA mRNA induction levels.
  - text: >-
      As we reported previously, MNDA mRNA level in adherent monocytes is
      elevated by IFN-alpha; in this study, we further assessed MNDA expression
      in in vitro monocyte-derived macrophages.
pipeline_tag: token-classification
co2_eq_emissions:
  emissions: 45.104
  source: codecarbon
  training_type: fine-tuning
  on_cloud: false
  gpu_model: 1 x NVIDIA GeForce RTX 3090
  cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
  ram_total_size: 31.777088165283203
  hours_used: 0.296
model-index:
  - name: SpanMarker with bert-base-uncased on BioNLP2004
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: BioNLP2004
          type: tner/bionlp2004
          split: test
        metrics:
          - type: f1
            value: 0.7620637836032726
            name: F1
          - type: precision
            value: 0.7289958470876371
            name: Precision
          - type: recall
            value: 0.7982742537313433
            name: Recall

SpanMarker with bert-base-uncased on BioNLP2004

This is a SpanMarker model trained on the BioNLP2004 dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-uncased as the underlying encoder. See train.py for the training script.

Model Details

Model Description

  • Model Type: SpanMarker
  • Encoder: bert-base-uncased
  • Maximum Sequence Length: 256 tokens
  • Maximum Entity Length: 8 words
  • Training Dataset: BioNLP2004
  • Language: en
  • License: other

Model Sources

Model Labels

Label Examples
DNA "immunoglobulin heavy-chain enhancer", "enhancer", "immunoglobulin heavy-chain ( IgH ) enhancer"
RNA "GATA-1 mRNA", "c-myb mRNA", "antisense myb RNA"
cell_line "monocytic U937 cells", "TNF-treated HUVECs", "HUVECs"
cell_type "B cells", "non-B cells", "human red blood cells"
protein "ICAM-1", "VCAM-1", "NADPH oxidase"

Evaluation

Metrics

Label Precision Recall F1
all 0.7290 0.7983 0.7621
DNA 0.7174 0.7505 0.7336
RNA 0.6977 0.7692 0.7317
cell_line 0.5831 0.7020 0.6370
cell_type 0.8222 0.7381 0.7779
protein 0.7196 0.8407 0.7755

Uses

Direct Use 

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-bionlp")
# Run inference
entities = model.predict("In erythroid cells most of the transcription activity was contained in a 150 bp promoter fragment with binding sites for transcription factors AP2, Sp1 and the erythroid-specific GATA-1.")

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-bionlp")

# 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-bionlp-finetuned")

Training Details

Training Set Metrics

Training set Min Median Max
Sentence length 2 26.5790 166
Entities per sentence 0 2.7528 23

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
0.4505 300 0.0210 0.7497 0.7659 0.7577 0.9254
0.9009 600 0.0162 0.8048 0.8217 0.8131 0.9432
1.3514 900 0.0154 0.8126 0.8249 0.8187 0.9434
1.8018 1200 0.0149 0.8148 0.8451 0.8296 0.9481
2.2523 1500 0.0150 0.8297 0.8438 0.8367 0.9501
2.7027 1800 0.0145 0.8280 0.8443 0.8361 0.9501

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Carbon Emitted: 0.045 kg of CO2
  • Hours Used: 0.296 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 : 4.29.2
  • PyTorch: 2.0.1+cu118
  • Datasets: 2.14.3
  • Tokenizers: 0.13.2