finbert-tone-finetuned-finance-topic-classification

This model is a fine-tuned version of yiyanghkust/finbert-tone on Twitter Financial News Topic dataset. It achieves the following results on the evaluation set:

Loss: 0.509021
Accuracy: 0.910615
F1: 0.910647
Precision: 0.911335
Recall: 0.910615

Model description

Model determines the financial topic of given tweets over 20 various topics. Given the unbalanced distribution of the class labels, the weights were adjusted to pay attention to the less sampled labels which should increase overall performance..

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: 64
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
No log	1.0	266	0.5152	0.8552	0.8504	0.8508	0.8552
0.7618	2.0	532	0.3999	0.8790	0.8781	0.8842	0.8790
0.7618	3.0	798	0.3628	0.8943	0.8940	0.8958	0.8943
0.16	4.0	1064	0.3776	0.8997	0.9001	0.9025	0.8997
0.16	5.0	1330	0.4286	0.8999	0.9002	0.9022	0.8999
0.058	6.0	1596	0.4500	0.9043	0.9042	0.9055	0.9043
0.058	7.0	1862	0.4689	0.9021	0.9017	0.9026	0.9021
0.0267	8.0	2128	0.4918	0.9031	0.9029	0.9039	0.9031
0.0267	9.0	2394	0.5030	0.9048	0.9049	0.9060	0.9048
0.0177	10.0	2660	0.5052	0.9033	0.9034	0.9044	0.9033
0.0177	11.0	2926	0.5265	0.9036	0.9034	0.9055	0.9036
0.013	12.0	3192	0.5267	0.9041	0.9041	0.9058	0.9041
0.013	13.0	3458	0.5090	0.9106	0.9106	0.9113	0.9106
0.0105	14.0	3724	0.5315	0.9067	0.9067	0.9080	0.9067
0.0105	15.0	3990	0.5339	0.9084	0.9084	0.9093	0.9084
0.0068	16.0	4256	0.5414	0.9072	0.9074	0.9088	0.9072
0.0051	17.0	4522	0.5460	0.9092	0.9091	0.9102	0.9092
0.0051	18.0	4788	0.5438	0.9072	0.9073	0.9081	0.9072
0.0035	19.0	5054	0.5474	0.9072	0.9073	0.9080	0.9072
0.0035	20.0	5320	0.5484	0.9079	0.9080	0.9087	0.9079

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2