metadata

tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: finbert-tone-finetuned-fintwitter-classification
    results: []

finbert-tone-finetuned-fintwitter-classification

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

Loss: 1.4236
Accuracy: 0.8823
F1: 0.8823
Precision: 0.8825
Recall: 0.8823

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: 16
eval_batch_size: 16
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
0.6385	1.0	597	0.3688	0.8668	0.8693	0.8744	0.8668
0.3044	2.0	1194	0.3994	0.8744	0.8726	0.8739	0.8744
0.1833	3.0	1791	0.6212	0.8781	0.8764	0.8762	0.8781
0.1189	4.0	2388	0.8370	0.8740	0.8743	0.8748	0.8740
0.0759	5.0	2985	0.9107	0.8807	0.8798	0.8796	0.8807
0.0291	6.0	3582	0.9711	0.8836	0.8825	0.8821	0.8836
0.0314	7.0	4179	1.1305	0.8819	0.8811	0.8812	0.8819
0.0217	8.0	4776	1.0190	0.8811	0.8813	0.8816	0.8811
0.0227	9.0	5373	1.1940	0.8844	0.8832	0.8838	0.8844
0.0156	10.0	5970	1.2595	0.8752	0.8768	0.8801	0.8752
0.0135	11.0	6567	1.1931	0.8760	0.8768	0.8780	0.8760
0.009	12.0	7164	1.2154	0.8857	0.8852	0.8848	0.8857
0.0058	13.0	7761	1.3874	0.8748	0.8759	0.8776	0.8748
0.009	14.0	8358	1.4193	0.8740	0.8754	0.8780	0.8740
0.0042	15.0	8955	1.2999	0.8807	0.8800	0.8796	0.8807
0.0028	16.0	9552	1.3428	0.8802	0.8805	0.8817	0.8802
0.0029	17.0	10149	1.3959	0.8807	0.8807	0.8810	0.8807
0.0022	18.0	10746	1.4149	0.8827	0.8823	0.8824	0.8827
0.0037	19.0	11343	1.4078	0.8840	0.8838	0.8838	0.8840
0.001	20.0	11940	1.4236	0.8823	0.8823	0.8825	0.8823

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2