metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: distilbert-undersampled
    results: []

distilbert-undersampled

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0826
Accuracy: 0.9811
F1: 0.9810
Recall: 0.9811
Precision: 0.9812

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: 3e-05
train_batch_size: 16
eval_batch_size: 16
seed: 33
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.0959	0.2	2000	0.0999	0.9651	0.9628	0.9651	0.9655
0.0618	0.41	4000	0.0886	0.9717	0.9717	0.9717	0.9731
0.159	0.61	6000	0.0884	0.9719	0.9720	0.9719	0.9728
0.0513	0.81	8000	0.0785	0.9782	0.9782	0.9782	0.9788
0.0219	1.01	10000	0.0680	0.9779	0.9779	0.9779	0.9783
0.036	1.22	12000	0.0745	0.9787	0.9787	0.9787	0.9792
0.0892	1.42	14000	0.0675	0.9786	0.9786	0.9786	0.9789
0.0214	1.62	16000	0.0760	0.9799	0.9798	0.9799	0.9801
0.0882	1.83	18000	0.0800	0.9800	0.9800	0.9800	0.9802
0.0234	2.03	20000	0.0720	0.9813	0.9813	0.9813	0.9815
0.0132	2.23	22000	0.0738	0.9803	0.9803	0.9803	0.9805
0.0136	2.43	24000	0.0847	0.9804	0.9804	0.9804	0.9806
0.0119	2.64	26000	0.0826	0.9811	0.9810	0.9811	0.9812

Framework versions

Transformers 4.16.2
Pytorch 1.10.0+cu111
Datasets 1.18.3
Tokenizers 0.11.0