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# xtreme_s_xlsr_300m_minds14

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/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

{"language": ["all"], "license": "apache-2.0", "tags": ["minds14", "google/xtreme_s", "generated_from_trainer"], "datasets": ["google/xtreme_s"], "metrics": ["f1", "accuracy"], "model-index": [{"name": "xtreme_s_xlsr_300m_minds14", "results": []}]}
transformers pytorch tensorboard wav2vec2 audio-classification minds14 google/xtreme_s generated_from_trainer all dataset:google/xtreme_s license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #minds14 #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us 

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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.2+cu113
* Datasets 2.0.1.dev0
* Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #minds14 #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.18.0.dev0
* Pytorch 1.10.2+cu113
* Datasets 2.0.1.dev0
* Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #minds14 #google/xtreme_s #generated_from_trainer #all #dataset-google/xtreme_s #license-apache-2.0 #endpoints_compatible #region-us 
### 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### Framework versions


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