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# bert-base-vi

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the vietnamese_students_feedback dataset.

## Intended uses & limitations

This model is used for classifying the semantic of student's feedback on teachers, lectures,...

It's used on Vietnamese language only.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
{"language": ["vi"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["vietnamese_students_feedback"], "pipeline_tag": "text-classification", "model-index": [{"name": "bert-base-vi", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer vi dataset:vietnamese_students_feedback license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #vi #dataset-vietnamese_students_feedback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# bert-base-vi

This model is a fine-tuned version of bert-base-uncased on the vietnamese_students_feedback dataset.

## Intended uses & limitations

This model is used for classifying the semantic of student's feedback on teachers, lectures,...

It's used on Vietnamese language only.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
# bert-base-vi

This model is a fine-tuned version of bert-base-uncased on the vietnamese_students_feedback dataset. ## Intended uses & limitations

This model is used for classifying the semantic of student's feedback on teachers, lectures,...

It's used on Vietnamese language only. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #vi #dataset-vietnamese_students_feedback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # bert-base-vi

This model is a fine-tuned version of bert-base-uncased on the vietnamese_students_feedback dataset. ## Intended uses & limitations

This model is used for classifying the semantic of student's feedback on teachers, lectures,...

It's used on Vietnamese language only. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #vi #dataset-vietnamese_students_feedback #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# bert-base-vi

This model is a fine-tuned version of bert-base-uncased on the vietnamese_students_feedback dataset.## Intended uses & limitations

This model is used for classifying the semantic of student's feedback on teachers, lectures,...

It's used on Vietnamese language only.### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2