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: 0.7428571428571428
          - name: Accuracy
            type: accuracy
            value: 0.2

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: 0.3066
F1: 0.7429
Roc Auc: 0.8142
Accuracy: 0.2

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
0.7601	1.0	12	0.6966	0.2564	0.4518	0.0
0.6757	2.0	24	0.5629	0.6667	0.7785	0.0
0.5796	3.0	36	0.4652	0.6286	0.7477	0.0
0.5026	4.0	48	0.4161	0.6479	0.7605	0.0
0.4282	5.0	60	0.3830	0.6849	0.7862	0.0
0.4085	6.0	72	0.3658	0.7273	0.7962	0.0
0.3847	7.0	84	0.3538	0.7353	0.8052	0.0
0.3829	8.0	96	0.3457	0.6761	0.7772	0.0
0.3758	9.0	108	0.3409	0.6857	0.7810	0.0
0.3487	10.0	120	0.3327	0.7143	0.7976	0.0
0.3421	11.0	132	0.3268	0.6866	0.7758	0.0
0.3351	12.0	144	0.3183	0.7059	0.7886	0.0
0.3245	13.0	156	0.3149	0.7246	0.8014	0.0
0.3191	14.0	168	0.3087	0.7246	0.8014	0.1
0.3083	15.0	180	0.3066	0.7429	0.8142	0.2
0.3061	16.0	192	0.3062	0.7429	0.8142	0.2
0.2935	17.0	204	0.3017	0.7429	0.8142	0.2
0.2888	18.0	216	0.3009	0.7429	0.8142	0.2
0.297	19.0	228	0.3022	0.7429	0.8142	0.2
0.2868	20.0	240	0.3014	0.7429	0.8142	0.2

Framework versions

Transformers 4.21.1
Pytorch 1.12.1
Datasets 2.4.0
Tokenizers 0.12.1