---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# distilbert-complaints-wandb

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/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