model_broadclass_onSet0

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.9207
• 0 Precision: 1.0
• 0 Recall: 1.0
• 0 F1-score: 1.0
• 0 Support: 31
• 1 Precision: 0.9615
• 1 Recall: 1.0
• 1 F1-score: 0.9804
• 1 Support: 25
• 2 Precision: 1.0
• 2 Recall: 0.9630
• 2 F1-score: 0.9811
• 2 Support: 27
• 3 Precision: 1.0
• 3 Recall: 1.0
• 3 F1-score: 1.0
• 3 Support: 15
• Accuracy: 0.9898
• Macro avg Precision: 0.9904
• Macro avg Recall: 0.9907
• Macro avg F1-score: 0.9904
• Macro avg Support: 98
• Weighted avg Precision: 0.9902
• Weighted avg Recall: 0.9898
• Weighted avg F1-score: 0.9898
• Weighted avg Support: 98
• Wer: 0.9344
• Mtrix: [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]]

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: 0.0003
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 200
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	0 Precision	0 Recall	0 F1-score	0 Support	1 Precision	1 Recall	1 F1-score	1 Support	2 Precision	2 Recall	2 F1-score	2 Support	3 Precision	3 Recall	3 F1-score	3 Support	Accuracy	Macro avg Precision	Macro avg Recall	Macro avg F1-score	Macro avg Support	Weighted avg Precision	Weighted avg Recall	Weighted avg F1-score	Weighted avg Support	Wer	Mtrix
2.3791	4.16	100	2.2297	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
2.276	8.33	200	2.1645	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.9646	12.49	300	1.9022	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.7089	16.65	400	1.6727	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.5546	20.82	500	1.5776	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.5671	24.98	600	1.5759	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.5548	29.16	700	1.5419	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.5148	33.33	800	1.4847	0.3263	1.0	0.4921	31	0.0	0.0	0.0	25	1.0	0.1111	0.2000	27	0.0	0.0	0.0	15	0.3469	0.3316	0.2778	0.1730	98	0.3787	0.3469	0.2108	98	0.9837	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 24, 0, 3, 0], [3, 15, 0, 0, 0]]
1.4234	37.49	900	1.4497	0.4429	1.0	0.6139	31	1.0	0.28	0.4375	25	1.0	0.5556	0.7143	27	1.0	0.4	0.5714	15	0.6020	0.8607	0.5589	0.5843	98	0.8238	0.6020	0.5900	98	0.9975	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 18, 7, 0, 0], [2, 12, 0, 15, 0], [3, 9, 0, 0, 6]]
1.3619	41.65	1000	1.3438	1.0	0.9677	0.9836	31	0.9259	1.0	0.9615	25	1.0	0.9630	0.9811	27	1.0	1.0	1.0	15	0.9796	0.9815	0.9827	0.9816	98	0.9811	0.9796	0.9798	98	0.9832	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]]
0.9703	45.82	1100	0.9444	1.0	1.0	1.0	31	0.9615	1.0	0.9804	25	1.0	0.9630	0.9811	27	1.0	1.0	1.0	15	0.9898	0.9904	0.9907	0.9904	98	0.9902	0.9898	0.9898	98	0.9289	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]]
0.9299	49.98	1200	0.9207	1.0	1.0	1.0	31	0.9615	1.0	0.9804	25	1.0	0.9630	0.9811	27	1.0	1.0	1.0	15	0.9898	0.9904	0.9907	0.9904	98	0.9902	0.9898	0.9898	98	0.9344	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 26, 0], [3, 0, 0, 0, 15]]

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2