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SpanMarker with bert-base-uncased on Acronym Identification

This is a SpanMarker model trained on the Acronym Identification 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.

Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance: tomaarsen/span-marker-bert-base-acronyms.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
long "successive convex approximation", "controlled natural language", "Conversational Question Answering"
short "SODA", "CNL", "CoQA"

Evaluation

Metrics

Label Precision Recall F1
all 0.9339 0.9063 0.9199
long 0.9314 0.8845 0.9074
short 0.9352 0.9174 0.9262

Uses

Direct Use for Inference

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-uncased-acronyms")
# Run inference
entities = model.predict("compression algorithms like principal component analysis (pca) can reduce noise and complexity.")

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-acronyms")

# 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-acronyms-finetuned")

Training Details

Training Set Metrics

Training set Min Median Max
Sentence length 4 32.3372 170
Entities per sentence 0 2.6775 24

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: 2

Training Results

Epoch Step Validation Loss Validation Precision Validation Recall Validation F1 Validation Accuracy
0.3120 200 0.0097 0.8999 0.8731 0.8863 0.9718
0.6240 400 0.0075 0.9163 0.8995 0.9078 0.9769
0.9360 600 0.0076 0.9079 0.9153 0.9116 0.9773
1.2480 800 0.0069 0.9267 0.9006 0.9135 0.9778
1.5601 1000 0.0065 0.9268 0.9044 0.9154 0.9782
1.8721 1200 0.0065 0.9279 0.9061 0.9168 0.9787

Environmental Impact

Carbon emissions were measured using CodeCarbon.

  • Carbon Emitted: 0.031 kg of CO2
  • Hours Used: 0.272 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: 4.30.0
  • 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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Finetuned from

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

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

Evaluation results