This model is a fine-tuned version of distilbert-base-uncased on the financial_phrasebank dataset. It achieves the following results on the evaluation set:

Loss: 0.0937
Accuracy: 0.9779
F1: 0.9780

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: 2e-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
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.6418	1.0	57	0.3340	0.8805	0.8768
0.1821	2.0	114	0.1088	0.9690	0.9691
0.0795	3.0	171	0.0822	0.9823	0.9823
0.0385	4.0	228	0.0939	0.9646	0.9646
0.0218	5.0	285	0.1151	0.9735	0.9737
0.0149	6.0	342	0.1126	0.9690	0.9694
0.006	7.0	399	0.0989	0.9779	0.9780
0.0093	8.0	456	0.1009	0.9779	0.9780
0.0063	9.0	513	0.0899	0.9779	0.9780
0.0039	10.0	570	0.0937	0.9779	0.9780

Framework versions

Transformers 4.17.0
Pytorch 1.11.0+cpu
Datasets 2.3.2
Tokenizers 0.12.1

Farshid/distilbert-base-uncased_allagree3

distilbert-base-uncased_allagree3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Farshid/
distilbert-base-uncased_allagree3