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---
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-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: 0.8166760103970236
- name: F1
type: f1
value: 0.8089132637288794
- name: Recall
type: recall
value: 0.8166760103970236
- name: Precision
type: precision
value: 0.810259366582512
---
<!-- 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-1024
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: 0.5664
- Accuracy: 0.8167
- F1: 0.8089
- Recall: 0.8167
- Precision: 0.8103
## 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: 2.9291066722689668e-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.7592 | 0.61 | 1500 | 0.6981 | 0.7776 | 0.7495 | 0.7776 | 0.7610 |
| 0.5859 | 1.22 | 3000 | 0.6082 | 0.8085 | 0.7990 | 0.8085 | 0.8005 |
| 0.5228 | 1.83 | 4500 | 0.5664 | 0.8167 | 0.8089 | 0.8167 | 0.8103 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|