README.md

metadata

license: apache-2.0
library_name: span-marker
tags:
  - span-marker
  - token-classification
  - ner
  - named-entity-recognition
pipeline_tag: token-classification
widget:
  - text: >-
      X-Linked adrenoleukodystrophy (ALD) is a genetic disease associated with
      demyelination of the central nervous system, adrenal insufficiency, and
      accumulation of very long chain fatty acids in tissue and body fluids.
    example_title: Example 1
  - text: >-
      Canavan disease is inherited as an autosomal recessive trait that is
      caused by the deficiency of aspartoacylase (ASPA).
    example_title: Example 2
  - text: >-
      However, both models lack other frequent DM symptoms including the
      fibre-type dependent atrophy, myotonia, cataract and male-infertility.
    example_title: Example 3
model-index:
  - name: SpanMarker w. bert-base-cased on NCBI Disease by Tom Aarsen
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          type: ncbi_disease
          name: NCBI Disease
          split: test
          revision: acd0e6451198d5b615c12356ab6a05fff4610920
        metrics:
          - type: f1
            value: 0.8813
            name: F1
          - type: precision
            value: 0.8661
            name: Precision
          - type: recall
            value: 0.8971
            name: Recall
datasets:
  - ncbi_disease
language:
  - en
metrics:
  - f1
  - recall
  - precision

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

Label	Examples
DISEASE	"ataxia-telangiectasia", "T-cell leukaemia", "C5D", "neutrophilic leukocytosis", "pyogenic infection"

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

Training Loss	Epoch	Step	Validation Loss	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0038	1.41	300	0.0059	0.8141	0.8579	0.8354	0.9818
0.0018	2.82	600	0.0054	0.8315	0.8720	0.8513	0.9840

Framework versions

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