Kayvane's picture
update model card README.md
9f38c77
metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - consumer-finance-complaints
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: distilroberta-base-wandb-week-3-complaints-classifier-1024
    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: 0.8279904184292339
          - name: F1
            type: f1
            value: 0.8236604095677945
          - name: Recall
            type: recall
            value: 0.8279904184292339
          - name: Precision
            type: precision
            value: 0.8235526237070518

distilroberta-base-wandb-week-3-complaints-classifier-1024

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

  • Loss: 0.5351
  • Accuracy: 0.8280
  • F1: 0.8237
  • Recall: 0.8280
  • Precision: 0.8236

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: 9.027176214786854e-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: 1024
  • num_epochs: 2
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Recall Precision
0.7756 0.61 1500 0.7411 0.7647 0.7375 0.7647 0.7606
0.5804 1.22 3000 0.6140 0.8088 0.8052 0.8088 0.8077
0.5008 1.83 4500 0.5351 0.8280 0.8237 0.8280 0.8236

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.11.0+cu102
  • Datasets 2.3.2
  • Tokenizers 0.12.1