wav2vec_base_crema_sentiment_analysis

This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:

• Top3 Accuracy: 0.9450
• Loss: 0.8840
• Accuracy: 0.7087

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: 1e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Top3 Accuracy	Validation Loss	Accuracy
1.7821	0.9829	43	0.6711	1.7729	0.2957
1.6948	1.9886	87	0.7643	1.6510	0.3513
1.4876	2.9943	131	0.8593	1.4541	0.4615
1.3657	4.0	175	0.9095	1.3136	0.5421
1.2277	4.9829	218	0.9220	1.2109	0.5824
1.1083	5.9886	262	0.9203	1.1539	0.6030
1.0069	6.9943	306	0.9382	1.0568	0.6496
0.9566	8.0	350	0.9337	1.0153	0.6667
0.8801	8.9829	393	0.9373	0.9970	0.6622
0.8529	9.9886	437	0.9346	0.9792	0.6720
0.7565	10.9943	481	0.9471	0.9475	0.6882
0.7427	12.0	525	0.9462	0.9413	0.6783
0.6616	12.9829	568	0.9516	0.9155	0.6980
0.6539	13.9886	612	0.9543	0.9015	0.6944
0.6036	14.9943	656	0.9471	0.8954	0.6962
0.607	16.0	700	0.9507	0.9088	0.7007
0.5829	16.9829	743	0.9471	0.8934	0.7043
0.5772	17.9886	787	0.9543	0.9182	0.6837
0.5332	18.9943	831	0.9552	0.8802	0.7052
0.5096	20.0	875	0.9525	0.9697	0.6676
0.524	20.9829	918	0.9588	0.8813	0.7061
0.5195	21.9886	962	0.9588	0.8753	0.7142
0.4594	22.9943	1006	0.9552	0.9003	0.7007
0.4478	24.0	1050	0.9561	0.8869	0.6998
0.4578	24.9829	1093	0.9624	0.8874	0.7070
0.4516	25.9886	1137	0.9606	0.8648	0.7142
0.4574	26.9943	1181	0.9597	0.8755	0.7133
0.4093	28.0	1225	0.9615	0.8804	0.7043
0.4216	28.9829	1268	0.9606	0.8814	0.7088
0.4257	29.4857	1290	0.9606	0.8805	0.7097

Framework versions

• Transformers 4.40.0
• Pytorch 2.2.1+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1