SpanMarker for Disease Named Entity Recognition

This is a SpanMarker model trained on the ncbi_disease dataset. In particular, this SpanMarker model uses bert-base-cased as the underlying encoder. See train.py for the training script.

Metrics

This model achieves the following results on the testing set:

• Overall Precision: 0.8661
• Overall Recall: 0.8971
• Overall F1: 0.8813
• Overall Accuracy: 0.9837

Labels

Usage

To use this model for inference, first install the span_marker library:

pip install span_marker

You can then run inference with this model like so:

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-ncbi-disease")
# Run inference
entities = model.predict("Canavan disease is inherited as an autosomal recessive trait that is caused by the deficiency of aspartoacylase (ASPA).")

See the SpanMarker repository for documentation and additional information on this library.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• 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

Framework versions

• SpanMarker 1.2.4
• Transformers 4.31.0
• Pytorch 1.13.1+cu117
• Datasets 2.14.3
• Tokenizers 0.13.2