model_broadclass_onSet1.1

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: 0.2469
• 0 Precision: 1.0
• 0 Recall: 1.0
• 0 F1-score: 1.0
• 0 Support: 24
• 1 Precision: 1.0
• 1 Recall: 1.0
• 1 F1-score: 1.0
• 1 Support: 39
• 2 Precision: 1.0
• 2 Recall: 1.0
• 2 F1-score: 1.0
• 2 Support: 23
• 3 Precision: 1.0
• 3 Recall: 1.0
• 3 F1-score: 1.0
• 3 Support: 12
• Accuracy: 1.0
• Macro avg Precision: 1.0
• Macro avg Recall: 1.0
• Macro avg F1-score: 1.0
• Macro avg Support: 98
• Weighted avg Precision: 1.0
• Weighted avg Recall: 1.0
• Weighted avg F1-score: 1.0
• Weighted avg Support: 98
• Wer: 0.2423
• Mtrix: [[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]

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: 80
• 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
2.3722	4.16	100	2.1950	0.2449	1.0	0.3934	24	0.0	0.0	0.0	39	0.0	0.0	0.0	23	0.0	0.0	0.0	12	0.2449	0.0612	0.25	0.0984	98	0.0600	0.2449	0.0964	98	0.9879	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]]
2.2944	8.33	200	2.1537	0.2449	1.0	0.3934	24	0.0	0.0	0.0	39	0.0	0.0	0.0	23	0.0	0.0	0.0	12	0.2449	0.0612	0.25	0.0984	98	0.0600	0.2449	0.0964	98	0.9879	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]]
1.9927	12.49	300	1.8879	0.2449	1.0	0.3934	24	0.0	0.0	0.0	39	0.0	0.0	0.0	23	0.0	0.0	0.0	12	0.2449	0.0612	0.25	0.0984	98	0.0600	0.2449	0.0964	98	0.9879	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]]
1.7175	16.65	400	1.6374	0.2449	1.0	0.3934	24	0.0	0.0	0.0	39	0.0	0.0	0.0	23	0.0	0.0	0.0	12	0.2449	0.0612	0.25	0.0984	98	0.0600	0.2449	0.0964	98	0.9879	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]]
1.6065	20.82	500	1.5619	0.2449	1.0	0.3934	24	0.0	0.0	0.0	39	0.0	0.0	0.0	23	0.0	0.0	0.0	12	0.2449	0.0612	0.25	0.0984	98	0.0600	0.2449	0.0964	98	0.9879	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]]
1.5362	24.98	600	1.5019	0.2449	1.0	0.3934	24	0.0	0.0	0.0	39	0.0	0.0	0.0	23	0.0	0.0	0.0	12	0.2449	0.0612	0.25	0.0984	98	0.0600	0.2449	0.0964	98	0.9879	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]]
1.5599	29.16	700	1.4858	0.2449	1.0	0.3934	24	0.0	0.0	0.0	39	0.0	0.0	0.0	23	0.0	0.0	0.0	12	0.2449	0.0612	0.25	0.0984	98	0.0600	0.2449	0.0964	98	0.9879	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 39, 0, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]]
1.5344	33.33	800	1.4721	0.2759	1.0	0.4324	24	1.0	0.2821	0.4400	39	0.0	0.0	0.0	23	0.0	0.0	0.0	12	0.3571	0.3190	0.3205	0.2181	98	0.4655	0.3571	0.2810	98	0.9919	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 28, 11, 0, 0], [2, 23, 0, 0, 0], [3, 12, 0, 0, 0]]
1.4024	37.49	900	1.3532	1.0	1.0	1.0	24	1.0	1.0	1.0	39	1.0	1.0	1.0	23	1.0	1.0	1.0	12	1.0	1.0	1.0	1.0	98	1.0	1.0	1.0	98	0.9742	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]
0.9429	41.65	1000	0.9455	0.96	1.0	0.9796	24	0.9744	0.9744	0.9744	39	1.0	0.9565	0.9778	23	1.0	1.0	1.0	12	0.9796	0.9836	0.9827	0.9829	98	0.9800	0.9796	0.9796	98	0.9084	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 1, 22, 0], [3, 0, 0, 0, 12]]
0.8955	45.82	1100	0.8890	0.96	1.0	0.9796	24	1.0	0.9744	0.9870	39	1.0	1.0	1.0	23	1.0	1.0	1.0	12	0.9898	0.99	0.9936	0.9917	98	0.9902	0.9898	0.9898	98	0.9246	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]
0.8708	49.98	1200	0.8304	1.0	1.0	1.0	24	0.975	1.0	0.9873	39	1.0	0.9565	0.9778	23	1.0	1.0	1.0	12	0.9898	0.9938	0.9891	0.9913	98	0.9901	0.9898	0.9897	98	0.9272	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 1, 22, 0], [3, 0, 0, 0, 12]]
0.8671	54.16	1300	0.8028	0.96	1.0	0.9796	24	1.0	1.0	1.0	39	1.0	0.9565	0.9778	23	1.0	1.0	1.0	12	0.9898	0.99	0.9891	0.9893	98	0.9902	0.9898	0.9898	98	0.9211	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 1, 0, 22, 0], [3, 0, 0, 0, 12]]
0.8383	58.33	1400	0.7804	1.0	1.0	1.0	24	1.0	1.0	1.0	39	1.0	1.0	1.0	23	1.0	1.0	1.0	12	1.0	1.0	1.0	1.0	98	1.0	1.0	1.0	98	0.9170	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]
0.7872	62.49	1500	0.7745	0.96	1.0	0.9796	24	1.0	0.9744	0.9870	39	1.0	1.0	1.0	23	1.0	1.0	1.0	12	0.9898	0.99	0.9936	0.9917	98	0.9902	0.9898	0.9898	98	0.9439	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]
0.7538	66.65	1600	0.7141	0.96	1.0	0.9796	24	1.0	0.9744	0.9870	39	1.0	1.0	1.0	23	1.0	1.0	1.0	12	0.9898	0.99	0.9936	0.9917	98	0.9902	0.9898	0.9898	98	0.9267	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 1, 38, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]
0.6439	70.82	1700	0.5818	1.0	1.0	1.0	24	1.0	1.0	1.0	39	1.0	1.0	1.0	23	1.0	1.0	1.0	12	1.0	1.0	1.0	1.0	98	1.0	1.0	1.0	98	0.8574	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]
0.5295	74.98	1800	0.3775	1.0	1.0	1.0	24	1.0	1.0	1.0	39	1.0	1.0	1.0	23	1.0	1.0	1.0	12	1.0	1.0	1.0	1.0	98	1.0	1.0	1.0	98	0.4633	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]
0.4184	79.16	1900	0.2507	1.0	1.0	1.0	24	1.0	1.0	1.0	39	1.0	1.0	1.0	23	1.0	1.0	1.0	12	1.0	1.0	1.0	1.0	98	1.0	1.0	1.0	98	0.2529	[[0, 1, 2, 3], [0, 24, 0, 0, 0], [1, 0, 39, 0, 0], [2, 0, 0, 23, 0], [3, 0, 0, 0, 12]]

Framework versions

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