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

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

Evaluation results