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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - text-classification
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+ - sentiment-analysis
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+ - vietnamese
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+ - vsfc
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+ - phobert
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+ language:
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+ - vi
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+ datasets:
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+ - uit-vsfc
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+ model-index:
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+ - name: VSFC Sentiment Classifier (PhoBERT)
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+ results:
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+ - task:
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+ type: text-classification
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+ name: Sentiment Analysis
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+ dataset:
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+ name: UIT-VSFC
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+ type: uit-vsfc
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+ metrics:
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+ - type: accuracy
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+ value: 85.3
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+ - type: f1
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+ value: 84.7
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+ ---
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+
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+ # VSFC Sentiment Classifier using PhoBERT
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+
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+ This model is fine-tuned from [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base) on the UIT-VSFC dataset for Vietnamese sentiment analysis.
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+
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+ ## 🧠 Model Details
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+
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+ - **Model type**: Transformer (BERT-based)
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+ - **Base model**: [`vinai/phobert-base`](https://huggingface.co/vinai/phobert-base)
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+ - **Fine-tuned task**: Sentence-level sentiment classification
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+ - **Target labels**: Positive, Negative, Neutral
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+ - **Tokenizer**: SentencePiece BPE
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+
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+ ## 📚 Training Data
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+
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+ - **Dataset**: [UIT-VSFC](https://drive.google.com/drive/folders/1xclbjHHK58zk2X6iqbvMPS2rcy9y9E0X)
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+ - **Language**: Vietnamese
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+ - **License**: Academic use
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+ - Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different research fields between sentiment analysis and education.
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+
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+ ## 🚀 How to Use
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+
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+ tokenizer = AutoTokenizer.from_pretrained("tmt3103/VSFC-sentiment-classify-phoBERT")
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+ model = AutoModelForSequenceClassification.from_pretrained("tmt3103/VSFC-sentiment-classify-phoBERT")
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+
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+ inputs = tokenizer("Giảng viên thân thiện dễ thương", return_tensors="pt")
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+ outputs = model(**inputs)
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+ predicted_class = outputs.logits.argmax(dim=-1).item()
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