roberta_sentiment_detection

This model is a fine-tuned version of FacebookAI/roberta-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0687
• Exact Match Accuracy: 0.3028
• F1 Micro: 0.4447
• F1 Macro: 0.3193
• Roc Auc Macro: 0.8205
• Avg Precision Macro: 0.5004
• F1 Class 0: 0.1963
• F1 Class 1: 0.2745
• F1 Class 2: 0.5010
• F1 Class 3: 0.0526
• F1 Class 4: 0.6405
• F1 Class 5: 0.5008
• F1 Class 6: 0.0697

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: 192
• eval_batch_size: 384
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Exact Match Accuracy	F1 Micro	F1 Macro	Roc Auc Macro	Avg Precision Macro	F1 Class 0	F1 Class 1	F1 Class 2	F1 Class 3	F1 Class 4	F1 Class 5	F1 Class 6
No log	1.0	181	0.0608	0.0089	0.0171	0.0547	0.7791	0.4369	0.0	0.0	0.3830	0.0	0.0	0.0	0.0
0.1293	2.0	362	0.0560	0.0263	0.0473	0.0933	0.8291	0.5394	0.0	0.0	0.4752	0.0	0.0183	0.1597	0.0
0.1293	3.0	543	0.0548	0.1775	0.2832	0.1947	0.8390	0.5507	0.0	0.0925	0.4988	0.0	0.4706	0.3013	0.0
0.0522	4.0	724	0.0559	0.1357	0.2243	0.1949	0.8361	0.5424	0.0	0.1292	0.5237	0.0	0.3202	0.3911	0.0
0.0522	5.0	905	0.0569	0.2144	0.3369	0.2268	0.8381	0.5407	0.0183	0.1503	0.5195	0.0	0.5441	0.3346	0.0209
0.0475	6.0	1086	0.0602	0.2424	0.3755	0.2568	0.8318	0.5309	0.0322	0.1852	0.5233	0.0026	0.5792	0.4505	0.0244
0.0475	7.0	1267	0.0643	0.2719	0.4104	0.2893	0.8292	0.5172	0.1155	0.2197	0.5055	0.0407	0.6027	0.5169	0.0243
0.0439	8.0	1448	0.0647	0.2873	0.4290	0.2979	0.8244	0.5123	0.1277	0.2786	0.5210	0.0156	0.6350	0.4730	0.0345
0.0439	9.0	1629	0.0672	0.2901	0.4315	0.3143	0.8228	0.5068	0.1593	0.2654	0.5155	0.0574	0.6244	0.4964	0.0816
0.0414	10.0	1810	0.0687	0.3028	0.4447	0.3193	0.8205	0.5004	0.1963	0.2745	0.5010	0.0526	0.6405	0.5008	0.0697

Framework versions

• Transformers 4.53.3
• Pytorch 2.6.0+cu124
• Datasets 4.1.1
• Tokenizers 0.21.2