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# bart-base-chinese-text-classification

This model was trained from scratch on the None dataset.

Test Accuracy: 0.7646

## 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: 2e-05
- 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
- num_epochs: 4

### Framework versions

- Transformers 4.28.1
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "model-index": [{"name": "bart-base-chinese-text-classification", "results": []}]}
transformers pytorch bart text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# bart-base-chinese-text-classification

This model was trained from scratch on the None dataset.

Test Accuracy: 0.7646

## 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: 2e-05
- 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
- num_epochs: 4

### Framework versions

- Transformers 4.28.1
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.3

# bart-base-chinese-text-classification

This model was trained from scratch on the None dataset.

Test Accuracy: 0.7646 ## 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: 2e-05
- 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
- num_epochs: 4 ### Framework versions

- Transformers 4.28.1
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #bart #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # bart-base-chinese-text-classification

This model was trained from scratch on the None dataset.

Test Accuracy: 0.7646 ## 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: 2e-05
- 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
- num_epochs: 4 ### Framework versions

- Transformers 4.28.1
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.3