PhoBERT-Emotion: Emotion Recognition for Vietnamese Text
This model is a fine-tuned version of vinai/phobert-base on the VSMEC dataset for emotion recognition in Vietnamese text. It achieves competitive performance on this task.
Model Details
- Base Model:
vinai/phobert-base - Dataset: 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
- Batch size:
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:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("visolex/phobert-emotion")
model = AutoModelForSequenceClassification.from_pretrained("visolex/phobert-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}")