metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- banking77
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-banking77
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: banking77
type: banking77
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.925018570680639
distilbert-base-uncased-finetuned-banking77
This model is a fine-tuned version of distilbert-base-uncased on the banking77 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2935
- Accuracy: 0.925
- F1: 0.9250
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: 9.686210354742596e-05
- train_batch_size: 64
- eval_batch_size: 32
- seed: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 126 | 1.1457 | 0.7896 | 0.7685 |
| No log | 2.0 | 252 | 0.4673 | 0.8906 | 0.8889 |
| No log | 3.0 | 378 | 0.3488 | 0.9150 | 0.9151 |
| 0.9787 | 4.0 | 504 | 0.3238 | 0.9180 | 0.9179 |
| 0.9787 | 5.0 | 630 | 0.3126 | 0.9225 | 0.9226 |
Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0
- Datasets 2.0.0
- Tokenizers 0.11.6