metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - filter_sort
metrics:
  - f1
  - accuracy
model-index:
  - name: favs-filtersort-multilabel-classification-bert-base-cased
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: filter_sort
          type: filter_sort
          config: default
          split: train
          args: default
        metrics:
          - name: F1
            type: f1
            value: 0.767123287671233
          - name: Accuracy
            type: accuracy
            value: 0.3

favs-filtersort-multilabel-classification-bert-base-cased

This model is a fine-tuned version of bert-base-cased on the filter_sort dataset. It achieves the following results on the evaluation set:

Loss: 0.2847
F1: 0.7671
Roc Auc: 0.8361
Accuracy: 0.3

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: 1.5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.7037	1.0	12	0.6056	0.5714	0.7341	0.0
0.5962	2.0	24	0.5212	0.5067	0.6787	0.0
0.5393	3.0	36	0.4396	0.6197	0.7439	0.1
0.4682	4.0	48	0.3963	0.5714	0.7079	0.0
0.409	5.0	60	0.3710	0.6061	0.7297	0.0
0.3923	6.0	72	0.3571	0.6286	0.7477	0.1
0.3682	7.0	84	0.3439	0.6849	0.7862	0.2
0.367	8.0	96	0.3286	0.6479	0.7605	0.1
0.3633	9.0	108	0.3194	0.6761	0.7772	0.2
0.3359	10.0	120	0.3145	0.6761	0.7772	0.2
0.3327	11.0	132	0.3054	0.6957	0.7848	0.2
0.3206	12.0	144	0.2998	0.7297	0.8156	0.2
0.3125	13.0	156	0.2926	0.7222	0.8066	0.2
0.3073	14.0	168	0.2847	0.7671	0.8361	0.3
0.2972	15.0	180	0.2819	0.7606	0.8271	0.3
0.2933	16.0	192	0.2773	0.7606	0.8271	0.3
0.2798	17.0	204	0.2752	0.7606	0.8271	0.3
0.2737	18.0	216	0.2731	0.7606	0.8271	0.3
0.2866	19.0	228	0.2720	0.7606	0.8271	0.3
0.2729	20.0	240	0.2713	0.7606	0.8271	0.3

Framework versions

Transformers 4.21.1
Pytorch 1.12.1
Datasets 2.4.0
Tokenizers 0.12.1