# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("risa-ai/sentiment-v2")
model = AutoModelForSequenceClassification.from_pretrained("risa-ai/sentiment-v2")Quick Links
Hyperparameters
"epochs": 32,
"learning_rate": 2e-5,
"batch_size_train": 24,
"batch_size_val": 16,
"max_len": 512,
"num_worker": 4,
"max_grad_norm": 5,
}
Run Summary Logs
accuracy 0.8318462096481682
f1 0.8314074021814951
precision 0.7324855145009559
recall 0.7052524442856924
train_loss 0.006222551137038769
Evaluation Result
precision recall f1-score support
confused 0.63 0.62 0.62 1170
negative 0.73 0.60 0.66 515
netral 0.89 0.90 0.89 9888
positive 0.70 0.70 0.70 2212
accuracy 0.83 13785
macro avg 0.73 0.70 0.72 13785
weighted avg 0.83 0.83 0.83 13785
Confusion Matrix
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="risa-ai/sentiment-v2")