metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - consumer-finance-complaints
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: distilbert-base-uncased-wandb-week-3-complaints-classifier-1500
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: consumer-finance-complaints
          type: consumer-finance-complaints
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8219254879975536
          - name: F1
            type: f1
            value: 0.8151998307079064
          - name: Recall
            type: recall
            value: 0.8219254879975536
          - name: Precision
            type: precision
            value: 0.8165753119578384

distilbert-base-uncased-wandb-week-3-complaints-classifier-1500

This model is a fine-tuned version of distilbert-base-uncased on the consumer-finance-complaints dataset. It achieves the following results on the evaluation set:

Loss: 0.5451
Accuracy: 0.8219
F1: 0.8152
Recall: 0.8219
Precision: 0.8166

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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1500
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
1.0678	0.2	500	0.9935	0.7193	0.6715	0.7193	0.6348
0.8447	0.41	1000	0.8331	0.7468	0.7108	0.7468	0.6990
0.7913	0.61	1500	0.7022	0.7770	0.7457	0.7770	0.7685
0.6973	0.82	2000	0.6584	0.7922	0.7710	0.7922	0.7849
0.5572	1.02	2500	0.6034	0.8076	0.7986	0.8076	0.7994
0.5528	1.22	3000	0.6017	0.8085	0.7986	0.8085	0.8063
0.5435	1.43	3500	0.5721	0.8147	0.8085	0.8147	0.8107
0.4995	1.63	4000	0.5598	0.8161	0.8125	0.8161	0.8144
0.4854	1.83	4500	0.5451	0.8219	0.8152	0.8219	0.8166

Framework versions

Transformers 4.20.1
Pytorch 1.11.0+cu102
Datasets 2.3.2
Tokenizers 0.12.1