xtreme_s_xlsr_300m_minds14

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME_S - MINDS14.ALL dataset. It achieves the following results on the evaluation set:

Accuracy: 0.9033
Accuracy Cs-cz: 0.9164
Accuracy De-de: 0.9477
Accuracy En-au: 0.9235
Accuracy En-gb: 0.9324
Accuracy En-us: 0.9326
Accuracy Es-es: 0.9177
Accuracy Fr-fr: 0.9444
Accuracy It-it: 0.9167
Accuracy Ko-kr: 0.8649
Accuracy Nl-nl: 0.9450
Accuracy Pl-pl: 0.9146
Accuracy Pt-pt: 0.8940
Accuracy Ru-ru: 0.8667
Accuracy Zh-cn: 0.7291
F1: 0.9015
F1 Cs-cz: 0.9154
F1 De-de: 0.9467
F1 En-au: 0.9199
F1 En-gb: 0.9334
F1 En-us: 0.9308
F1 Es-es: 0.9158
F1 Fr-fr: 0.9436
F1 It-it: 0.9135
F1 Ko-kr: 0.8642
F1 Nl-nl: 0.9440
F1 Pl-pl: 0.9159
F1 Pt-pt: 0.8883
F1 Ru-ru: 0.8646
F1 Zh-cn: 0.7249
Loss: 0.4119
Loss Cs-cz: 0.3790
Loss De-de: 0.2649
Loss En-au: 0.3459
Loss En-gb: 0.2853
Loss En-us: 0.2203
Loss Es-es: 0.2731
Loss Fr-fr: 0.1909
Loss It-it: 0.3520
Loss Ko-kr: 0.5431
Loss Nl-nl: 0.2515
Loss Pl-pl: 0.4113
Loss Pt-pt: 0.4798
Loss Ru-ru: 0.6470
Loss Zh-cn: 1.1216
Predict Samples: 4086

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	Accuracy
2.6739	5.41	200	2.5687	0.0430	0.1190
1.4953	10.81	400	1.6052	0.5550	0.5692
0.6177	16.22	600	0.7927	0.8052	0.8011
0.3609	21.62	800	0.5679	0.8609	0.8609
0.4972	27.03	1000	0.5944	0.8509	0.8523
0.1799	32.43	1200	0.6194	0.8623	0.8621
0.1308	37.84	1400	0.5956	0.8569	0.8548
0.2298	43.24	1600	0.5201	0.8732	0.8743
0.0052	48.65	1800	0.3826	0.9106	0.9103

Framework versions

Transformers 4.18.0.dev0
Pytorch 1.10.2+cu113
Datasets 2.0.1.dev0
Tokenizers 0.11.6

作者:

Anton Lozhkov

数据集大小:

1.18 GB