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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-complaints-wandb-product
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: 0.8690996641956535
- name: F1
type: f1
value: 0.8645310918904254
- name: Recall
type: recall
value: 0.8690996641956535
- name: Precision
type: precision
value: 0.8629318199420283
---
<!-- 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-product
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: 0.4431
- Accuracy: 0.8691
- F1: 0.8645
- Recall: 0.8691
- Precision: 0.8629
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.562 | 0.51 | 2000 | 0.5107 | 0.8452 | 0.8346 | 0.8452 | 0.8252 |
| 0.4548 | 1.01 | 4000 | 0.4628 | 0.8565 | 0.8481 | 0.8565 | 0.8466 |
| 0.3439 | 1.52 | 6000 | 0.4519 | 0.8605 | 0.8544 | 0.8605 | 0.8545 |
| 0.2626 | 2.03 | 8000 | 0.4412 | 0.8678 | 0.8618 | 0.8678 | 0.8626 |
| 0.2717 | 2.53 | 10000 | 0.4431 | 0.8691 | 0.8645 | 0.8691 | 0.8629 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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