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-512
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: 0.8038326283064064
- name: F1
type: f1
value: 0.791857014338201
- name: Recall
type: recall
value: 0.8038326283064064
- name: Precision
type: precision
value: 0.7922430702228043
distilroberta-base-wandb-week-3-complaints-classifier-512
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: 0.6004
- Accuracy: 0.8038
- F1: 0.7919
- Recall: 0.8038
- Precision: 0.7922
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: 1.7835312622444155e-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: 512
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.7559 | 0.61 | 1500 | 0.7307 | 0.7733 | 0.7411 | 0.7733 | 0.7286 |
| 0.6361 | 1.22 | 3000 | 0.6559 | 0.7846 | 0.7699 | 0.7846 | 0.7718 |
| 0.5774 | 1.83 | 4500 | 0.6004 | 0.8038 | 0.7919 | 0.8038 | 0.7922 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1