metadata
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-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: 0.6745323887671373
- name: F1
type: f1
value: 0.6355967633316707
- name: Recall
type: recall
value: 0.6745323887671373
- name: Precision
type: precision
value: 0.6122130681567332
distilbert-base-uncased-wandb-week-3-complaints-classifier-512
This model is a fine-tuned version of distilbert-base-uncased on the consumer-finance-complaints dataset. It achieves the following results on the evaluation set:
- Loss: 1.0839
- Accuracy: 0.6745
- F1: 0.6356
- Recall: 0.6745
- Precision: 0.6122
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: 0.0007879237562376572
- 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 |
|---|---|---|---|---|---|---|---|
| 1.2707 | 0.61 | 1500 | 1.3009 | 0.6381 | 0.5848 | 0.6381 | 0.5503 |
| 1.1348 | 1.22 | 3000 | 1.1510 | 0.6610 | 0.6178 | 0.6610 | 0.5909 |
| 1.0649 | 1.83 | 4500 | 1.0839 | 0.6745 | 0.6356 | 0.6745 | 0.6122 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1