ura-hcmut/UIT-VSFC
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How to use ZycckZ/Simple_VieQA-sentiment with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="ZycckZ/Simple_VieQA-sentiment") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("ZycckZ/Simple_VieQA-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("ZycckZ/Simple_VieQA-sentiment")Fine-tuned version of Simple_VieQA
for Vietnamese sentiment analysis on UIT-VSFC dataset.
| Metric | Score |
|---|---|
| Accuracy | 0.936 |
| F1-weighted | 0.930 |
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "ZycckZ/Simple_VieQA-sentiment"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
text = "Sản phẩm rất tốt, pin trâu"
inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
pred = torch.argmax(logits, dim=-1).item()
print(pred)