README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - f1
model-index:
  - name: xtreme_s_xlsr_minds14
    results: []

xtreme_s_xlsr_minds14

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.2566
• F1: {'f1': 0.9460569664921582, 'accuracy': 0.9468540012217471}

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: 32
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 64
• total_eval_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1500
• num_epochs: 50.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1
2.551	2.7	200	2.5921	{'f1': 0.03454307545755678, 'accuracy': 0.1148442272449603}
1.6934	5.41	400	1.5353	{'f1': 0.5831241711045994, 'accuracy': 0.6053756872327428}
0.5914	8.11	600	0.7337	{'f1': 0.7990425247664236, 'accuracy': 0.7947464874770922}
0.3896	10.81	800	0.5076	{'f1': 0.8738199236080776, 'accuracy': 0.872327428222358}
0.5052	13.51	1000	0.4917	{'f1': 0.8744760456867134, 'accuracy': 0.8747709224190593}
0.4806	16.22	1200	0.4751	{'f1': 0.8840798740258787, 'accuracy': 0.8845448992058644}
0.2103	18.92	1400	0.5228	{'f1': 0.8721632556623751, 'accuracy': 0.8729383017715333}
0.4198	21.62	1600	0.5910	{'f1': 0.8755207264572983, 'accuracy': 0.8766035430665852}
0.11	24.32	1800	0.4464	{'f1': 0.896423086249818, 'accuracy': 0.8955406230910201}
0.1233	27.03	2000	0.3760	{'f1': 0.9012283567348968, 'accuracy': 0.9016493585827734}
0.1827	29.73	2200	0.4178	{'f1': 0.9042381720184095, 'accuracy': 0.9059254734270006}
0.1235	32.43	2400	0.4152	{'f1': 0.9063257163259107, 'accuracy': 0.9071472205253512}
0.1873	35.14	2600	0.2903	{'f1': 0.9369340598806323, 'accuracy': 0.9376908979841173}
0.017	37.84	2800	0.3046	{'f1': 0.9300781160576355, 'accuracy': 0.9303604153940135}
0.0436	40.54	3000	0.3111	{'f1': 0.9315034391389341, 'accuracy': 0.9321930360415394}
0.0455	43.24	3200	0.2748	{'f1': 0.9417365311433034, 'accuracy': 0.9425778863775198}
0.046	45.95	3400	0.2800	{'f1': 0.9390712658440112, 'accuracy': 0.9395235186316433}
0.0042	48.65	3600	0.2566	{'f1': 0.9460569664921582, 'accuracy': 0.9468540012217471}

Framework versions

• Transformers 4.18.0.dev0
• Pytorch 1.10.2+cu113
• Datasets 1.18.4.dev0
• Tokenizers 0.11.6