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: 0.7660160032618113
- name: F1
type: f1
value: 0.7330865122537581
- name: Recall
type: recall
value: 0.7660160032618113
- name: Precision
type: precision
value: 0.709995727786744
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: 0.7911
- Accuracy: 0.7660
- F1: 0.7331
- Recall: 0.7660
- Precision: 0.7100
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: 0.0003536395504868732
- 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 |
---|---|---|---|---|---|---|---|
1.0283 | 0.61 | 1500 | 0.9504 | 0.7240 | 0.6937 | 0.7240 | 0.6779 |
0.9058 | 1.22 | 3000 | 0.8811 | 0.7446 | 0.7166 | 0.7446 | 0.6980 |
0.7803 | 1.83 | 4500 | 0.7911 | 0.7660 | 0.7331 | 0.7660 | 0.7100 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1