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---
license: apache-2.0
tags:
- text-classification
- topic-analysis
- vietnamese
- vsfc
- phobert
language:
- vi
datasets:
- uit-vsfc
model-index:
- name: VSFC Topic Classifier (PhoBERT)
results:
- task:
type: text-classification
name: Topic Classification
dataset:
name: UIT-VSFC
type: uit-vsfc
metrics:
- type: accuracy
value: 89.1346
- type: f1
value: 89.0436
---
# VSFC TOPIC Classifier using PhoBERT
This model is fine-tuned from [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base) on the UIT-VSFC dataset for Vietnamese Students Feedback Corpus topic analysis.
## 🧠 Model Details
- **Model type**: Transformer (BERT-based)
- **Base model**: [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base)
- **Fine-tuned task**: Sentence-level topc classification
- **Target labels**: Lecturer, Training program, Facility, Others
- **Tokenizer**: SentencePiece BPE
## 📚 Training Data
- **Dataset**: [UIT-VSFC](https://drive.google.com/drive/folders/1xclbjHHK58zk2X6iqbvMPS2rcy9y9E0X)
- **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
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tmt3103/VSFC-topic-classify-phoBERT")
model = AutoModelForSequenceClassification.from_pretrained("tmt3103/VSFC-topic-classify-phoBERT")
inputs = tokenizer("Giảng viên thân thiện dễ thương", return_tensors="pt")
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(dim=-1).item()
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