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---
language: vi
tags:
- emotion-recognition
- vietnamese
- bartpho
license: apache-2.0
datasets:
- VSMEC
metrics:
- accuracy
- f1
model-index:
- name: bartpho-emotion
results:
- task:
type: text-classification
name: Emotion Recognition
dataset:
name: VSMEC
type: custom
metrics:
- name: Accuracy
type: accuracy
value: <INSERT_ACCURACY>
- name: F1 Score
type: f1
value: <INSERT_F1_SCORE>
base_model:
- vinai/bartpho-syllable
pipeline_tag: text-classification
---
# bartpho-emotion: Emotion Recognition for Vietnamese Text
This model is a fine-tuned version of [`vinai/bartpho-syllable`](https://huggingface.co/vinai/bartpho-syllable) on the **VSMEC** dataset for emotion recognition in Vietnamese text. It achieves state-of-the-art performance on this task.
## Model Details
- **Base Model**: [`vinai/bartpho-syllable`](https://huggingface.co/vinai/bartpho-syllable)
- **Dataset**: [VSMEC](https://github.com/uitnlp/vsmec) (Vietnamese Social Media Emotion Corpus)
- **Fine-tuning Framework**: HuggingFace Transformers
- **Hyperparameters**:
- Batch size: `32`
- Learning rate: `5e-5`
- Epochs: `100`
- Max sequence length: `256`
## Dataset
The model was trained on the **VSMEC** dataset, which contains Vietnamese social media text annotated with emotion labels. The dataset includes the following emotion categories:
`{"Anger": 0, "Disgust": 1, "Enjoyment": 2, "Fear": 3, "Other": 4, "Sadness": 5, "Surprise": 6}`.
## Results
The model was evaluated using the following metrics:
- **Accuracy**: `<INSERT_ACCURACY>`
- **F1 Score**: `<INSERT_F1_SCORE>`
## Usage
You can use this model for emotion recognition in Vietnamese text. Below is an example of how to use it with the HuggingFace Transformers library:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("visolex/bartpho-emotion")
model = AutoModelForSequenceClassification.from_pretrained("visolex/bartpho-emotion")
text = "Tôi rất vui vì hôm nay trời đẹp!"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
outputs = model(**inputs)
predicted_class = outputs.logits.argmax(dim=-1).item()
print(f"Predicted emotion: {predicted_class}") |