metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - consumer-finance-complaints
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: distilbert-complaints-wandb
    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: 0.868877906608376
          - name: F1
            type: f1
            value: 0.8630522401242867
          - name: Recall
            type: recall
            value: 0.868877906608376
          - name: Precision
            type: precision
            value: 0.8616053523512515

distilbert-complaints-wandb

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: 0.4448
Accuracy: 0.8689
F1: 0.8631
Recall: 0.8689
Precision: 0.8616

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: 500
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.571	0.51	2000	0.5150	0.8469	0.8349	0.8469	0.8249
0.4765	1.01	4000	0.4676	0.8561	0.8451	0.8561	0.8376
0.3376	1.52	6000	0.4560	0.8609	0.8546	0.8609	0.8547
0.268	2.03	8000	0.4399	0.8684	0.8611	0.8684	0.8607
0.2654	2.53	10000	0.4448	0.8689	0.8631	0.8689	0.8616

Framework versions

Transformers 4.20.1
Pytorch 1.11.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1