metadata

tags:
  - generated_from_trainer
datasets:
  - jnlpba
metrics:
  - precision
  - recall
  - f1
  - accuracy
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
base_model: allenai/scibert_scivocab_uncased
model-index:
  - name: scibert-finetuned-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: jnlpba
          type: jnlpba
          config: jnlpba
          split: train
          args: jnlpba
        metrics:
          - type: precision
            value: 0.6737190414118119
            name: Precision
          - type: recall
            value: 0.7756869083352574
            name: Recall
          - type: f1
            value: 0.7211161792326267
            name: F1
          - type: accuracy
            value: 0.9226268866380928
            name: Accuracy

scibert-finetuned-ner

This model is a fine-tuned version of allenai/scibert_scivocab_uncased on the jnlpba dataset. It achieves the following results on the evaluation set:

Loss: 0.4717
Precision: 0.6737
Recall: 0.7757
F1: 0.7211
Accuracy: 0.9226

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
0.1608	1.0	2319	0.2431	0.6641	0.7581	0.7080	0.9250
0.103	2.0	4638	0.2916	0.6739	0.7803	0.7232	0.9228
0.0659	3.0	6957	0.3662	0.6796	0.7624	0.7186	0.9233
0.0393	4.0	9276	0.4222	0.6737	0.7771	0.7217	0.9225
0.025	5.0	11595	0.4717	0.6737	0.7757	0.7211	0.9226

Framework versions

Transformers 4.21.1
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1