Hugging Face

Select an Appropriate Base Model

Find a pretrained model that supports Vietnamese suitable for sentiment classification tasks.

Go to huggingface.co/models, filter by task 'Text Classification' and language 'vi', choose a model like PhoBERT or mBERT that has pre-fine-tuned capabilities.

Prioritize models with high download counts and recent updates, as they are often community-validated for better quality.

Prepare the Training Dataset

Label the sentiment (positive/negative/neutral) for a set of actual customer feedback.

Export the data to a CSV file consisting of two columns: text and label, manually label at least 500-1,000 samples.

Ensure a balanced number of samples between labels to avoid the model being biased towards one sentiment type.

Install Libraries and Load Data

Install Transformers and Datasets, then load the dataset in the appropriate format.

Run `pip install transformers datasets`, use `load_dataset('csv', data_files='data.csv')` to load the labeled data.

Split the data in an 80/20 ratio for the training and testing sets for objective accuracy evaluation.

Tokenize and Configure Training

Convert text into tokens suitable for the model, then set training parameters.

Use `AutoTokenizer.from_pretrained(model_name)` to tokenize, configure `TrainingArguments` with an appropriate learning_rate and epoch (typically 3-5 epochs).

Start with a small learning rate (2e-5) to prevent the model from forgetting the knowledge gained during pretraining.

Run Training and Evaluation

Initiate the fine-tuning process and monitor accuracy on the test set.

Call `Trainer(model=model, args=training_args, train_dataset=train, eval_dataset=test).train()` and then check the accuracy/F1 results.

If accuracy does not improve after several epochs, review the quality of the labeled data before adjusting parameters.

Upload Model to Hugging Face Hub or Deploy via Spaces

Save the fine-tuned model to the Hub for easy reuse or deploy a demo via Spaces.

Call `model.push_to_hub('company-name/sentiment-classification-model')`, then create a new Space and use Gradio to build a demo interface.

Set the repository to private if the training data contains sensitive customer information.