metadata
language:
- vi
base_model:
- vinai/phobert-base
A model fine-tuned for sentiment analysis based on vinai/phobert-base.
Labels:
- NEG: Negative
- POS: Positive
- NEU: Neutral
Dataset: Comments on Shoppe (https://shopee.vn/)
Usage
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("lamsytan/sentiment-analysis-base-phobert")
model = AutoModelForSequenceClassification.from_pretrained("lamsytan/sentiment-analysis-base-phobert")
sentence = "Áo đẹp lắm nhá lần sau sẽ ghé tiếp ạ"
inputs = tokenizer(sentence, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probabilities = torch.softmax(logits, dim=-1)
print(probabilities.tolist())
# Output:
# [[0.010827462188899517, 0.9538241624832153, 0.035348404198884964]]
# ^ ^ ^
# NEG POS NEU