ZycckZ/phobert-base_ABSA_finetuned

PhoBERT Base for Aspect-Based Sentiment Analysis (Vietnamese)

1. Model Description

This model is a fine-tuned version of vinai/phobert-base for the task of Aspect-Based Sentiment Analysis (ABSA) on Vietnamese text. It is optimized specifically for the Vietnamese language, providing high accuracy in identifying sentiments (Positive, Neutral, Negative) for specific aspects.

• Language: Vietnamese
• Model Type: Transformer-based Encoder
• Base Model: vinai/phobert-base

2. Training Data

The model was trained on Vietnamese ABSA datasets including thinhntr/absa and LuongPhan/UIT-ViSFD.

3. Training Procedure

Hyperparameters:

• Learning Rate: 5e-5
• Batch Size: 32
• Precision: Mixed precision (fp16)
• Epochs: 3

4. Evaluation Results

The model was evaluated on the test set (9141 samples).

• Accuracy: 88.92%
• F1-Macro: 83.73%

Classification Report:

Class	Precision	Recall	F1-Score
0 (Negative)	0.8854	0.9232	0.9039
1 (Neutral)	0.7376	0.6207	0.6741
2 (Positive)	0.9236	0.9446	0.9340

5. How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("ZycckZ/phobert-base_ABSA_finetuned")
model = AutoModelForSequenceClassification.from_pretrained("ZycckZ/phobert-base_ABSA_finetuned")