tmt3103/VSFC-sentiment-classify-phoBERT

VSFC Sentiment Classifier using PhoBERT

This model is fine-tuned from vinai/phobert-base on the UIT-VSFC dataset for Vietnamese sentiment analysis.

🧠 Model Details

• Model type: Transformer (BERT-based)
• Base model: vinai/phobert-base
• Fine-tuned task: Sentence-level sentiment classification
• Target labels: Positive, Negative, Neutral
• Tokenizer: SentencePiece BPE

📚 Training Data

• Dataset: UIT-VSFC
• Language: Vietnamese
• License: Academic use
• Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different research fields between sentiment analysis and education..

🚀 How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("tmt3103/VSFC-sentiment-classify-phoBERT")
model = AutoModelForSequenceClassification.from_pretrained("tmt3103/VSFC-sentiment-classify-phoBERT")

inputs = tokenizer("Giảng viên thân thiện dễ thương", return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(dim=-1).item()