Edit model card

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
Downloads last month
3
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train Kayvane/distilbert-complaints-wandb

Evaluation results