README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - bc5_cdr
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: electramed-small-BC5CDR-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: bc5_cdr
          type: bc5_cdr
          config: BC5CDR-Disease
          split: train
          args: BC5CDR-Disease
        metrics:
          - name: Precision
            type: precision
            value: 0.8091945430760605
          - name: Recall
            type: recall
            value: 0.882862677133245
          - name: F1
            type: f1
            value: 0.8444249427136657
          - name: Accuracy
            type: accuracy
            value: 0.9685851703406814

electramed-small-BC5CDR-ner

This model is a fine-tuned version of giacomomiolo/electramed_small_scivocab on the bc5_cdr dataset. It achieves the following results on the evaluation set:

• Loss: 0.1227
• Precision: 0.8092
• Recall: 0.8829
• F1: 0.8444
• Accuracy: 0.9686

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.7177	1.0	286	0.6902	0.0	0.0	0.0	0.8864
0.1561	2.0	572	0.3210	0.7334	0.8104	0.7700	0.9636
0.2511	3.0	858	0.2064	0.7809	0.8711	0.8236	0.9666
0.0512	4.0	1144	0.1599	0.7937	0.8751	0.8324	0.9689
0.083	5.0	1430	0.1449	0.7983	0.8804	0.8373	0.9679
0.0412	6.0	1716	0.1315	0.8141	0.8825	0.8469	0.9701
0.1437	7.0	2002	0.1258	0.8227	0.8758	0.8485	0.9699
0.1894	8.0	2288	0.1226	0.8141	0.8833	0.8473	0.9696
0.0236	9.0	2574	0.1220	0.8160	0.8824	0.8479	0.9694
0.0602	10.0	2860	0.1227	0.8092	0.8829	0.8444	0.9686

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1