metadata

tags:
  - generated_from_trainer
datasets:
  - jnlpba
widget:
  - text: >-
      The widespread circular form of DNA molecules inside cells creates very
      serious topological problems during replication. Due to the helical
      structure of the double helix the parental strands of circular DNA form a
      link of very high order, and yet they have to be unlinked before the cell
      division.
  - text: >-
      It consists of 25 exons encoding a 1,278-amino acid glycoprotein that is
      composed of 13 transmembrane domains
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: biobert-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: jnlpba
          type: jnlpba
          config: jnlpba
          split: train
          args: jnlpba
        metrics:
          - name: Precision
            type: precision
            value: 0.6550939663699308
          - name: Recall
            type: recall
            value: 0.7646040175479104
          - name: F1
            type: f1
            value: 0.7056253995312167
          - name: Accuracy
            type: accuracy
            value: 0.9107839603371846

biobert-finetuned-ner

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the jnlpba dataset. It achieves the following results on the evaluation set:

Loss: 0.5113
Precision: 0.6551
Recall: 0.7646
F1: 0.7056
Accuracy: 0.9108

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1815	1.0	2319	0.2706	0.6538	0.7704	0.7073	0.9160
0.1226	2.0	4638	0.3230	0.6524	0.7675	0.7053	0.9118
0.0813	3.0	6957	0.3974	0.6483	0.7611	0.7002	0.9101
0.0521	4.0	9276	0.4529	0.6575	0.7652	0.7073	0.9121
0.0356	5.0	11595	0.5113	0.6551	0.7646	0.7056	0.9108

Framework versions

Transformers 4.21.1
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1