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-256
    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: 0.8234544620559604
          - name: F1
            type: f1
            value: 0.8176243580045963
          - name: Recall
            type: recall
            value: 0.8234544620559604
          - name: Precision
            type: precision
            value: 0.8171438106054644

distilbert-base-uncased-wandb-week-3-complaints-classifier-256

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: 0.5453
Accuracy: 0.8235
F1: 0.8176
Recall: 0.8235
Precision: 0.8171

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: 4.097565552226687e-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: 256
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.6691	0.61	1500	0.6475	0.7962	0.7818	0.7962	0.7875
0.5361	1.22	3000	0.5794	0.8161	0.8080	0.8161	0.8112
0.4659	1.83	4500	0.5453	0.8235	0.8176	0.8235	0.8171

Framework versions

Transformers 4.20.1
Pytorch 1.11.0+cu102
Datasets 2.3.2
Tokenizers 0.12.1