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# hubert-base-common-language

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3477
- Accuracy: 0.7317

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 0
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 10.0
- training precision: Mixed Precision

### Training results



### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["audio-classification", "generated_from_trainer"], "datasets": ["common_language"], "metrics": ["accuracy"], "model-index": [{"name": "hubert-base-common-language", "results": []}]}
transformers pytorch safetensors optimum_graphcore hubert text-classification audio-classification generated_from_trainer dataset:common_language license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #optimum_graphcore #hubert #text-classification #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# hubert-base-common-language

This model is a fine-tuned version of facebook/hubert-base-ls960 on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3477
- Accuracy: 0.7317

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 0
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 10.0
- training precision: Mixed Precision

### Training results



### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6

# hubert-base-common-language

This model is a fine-tuned version of facebook/hubert-base-ls960 on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3477
- Accuracy: 0.7317 ## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 0
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 10.0
- training precision: Mixed Precision ### Training results ### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
TAGS
#transformers #pytorch #safetensors #optimum_graphcore #hubert #text-classification #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # hubert-base-common-language

This model is a fine-tuned version of facebook/hubert-base-ls960 on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3477
- Accuracy: 0.7317 ## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 0
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 10.0
- training precision: Mixed Precision ### Training results ### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #safetensors #optimum_graphcore #hubert #text-classification #audio-classification #generated_from_trainer #dataset-common_language #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# hubert-base-common-language

This model is a fine-tuned version of facebook/hubert-base-ls960 on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3477
- Accuracy: 0.7317## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 4
- seed: 0
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 10.0
- training precision: Mixed Precision### Training results### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cpu
- Datasets 2.0.0
- Tokenizers 0.11.6