---
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-1500
  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: 0.8219254879975536
    - name: F1
      type: f1
      value: 0.8151998307079064
    - name: Recall
      type: recall
      value: 0.8219254879975536
    - name: Precision
      type: precision
      value: 0.8165753119578384
---

<!-- 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-base-uncased-wandb-week-3-complaints-classifier-1500

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: 0.5451
- Accuracy: 0.8219
- F1: 0.8152
- Recall: 0.8219
- Precision: 0.8166

## 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: 1500
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.0678        | 0.2   | 500  | 0.9935          | 0.7193   | 0.6715 | 0.7193 | 0.6348    |
| 0.8447        | 0.41  | 1000 | 0.8331          | 0.7468   | 0.7108 | 0.7468 | 0.6990    |
| 0.7913        | 0.61  | 1500 | 0.7022          | 0.7770   | 0.7457 | 0.7770 | 0.7685    |
| 0.6973        | 0.82  | 2000 | 0.6584          | 0.7922   | 0.7710 | 0.7922 | 0.7849    |
| 0.5572        | 1.02  | 2500 | 0.6034          | 0.8076   | 0.7986 | 0.8076 | 0.7994    |
| 0.5528        | 1.22  | 3000 | 0.6017          | 0.8085   | 0.7986 | 0.8085 | 0.8063    |
| 0.5435        | 1.43  | 3500 | 0.5721          | 0.8147   | 0.8085 | 0.8147 | 0.8107    |
| 0.4995        | 1.63  | 4000 | 0.5598          | 0.8161   | 0.8125 | 0.8161 | 0.8144    |
| 0.4854        | 1.83  | 4500 | 0.5451          | 0.8219   | 0.8152 | 0.8219 | 0.8166    |


### Framework versions

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