from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "Bangkah/atha-text-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "produk ini bagus dan pengirimannya cepat"
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
with torch.no_grad():
    logits = model(**inputs).logits

probs = torch.softmax(logits, dim=-1)[0]
label_id = int(torch.argmax(probs).item())
label = model.config.id2label[label_id]
score = float(probs[label_id].item())
print({"label": label, "confidence": round(score, 4)})

Limitations

Dataset training masih sintetis, sehingga metrik tinggi tidak langsung merepresentasikan performa produksi.
Untuk use-case production, tetap lakukan fine-tuning ulang dengan data real domain aplikasi.

Validation Metrics

Loss: 0.0004
Accuracy: 1.0000
Macro F1: 1.0000

Confusion Matrix

true\pred	negative	neutral	positive
negative	100	0	0
neutral	0	100	0
positive	0	0	100

Classification Report

text

              precision    recall  f1-score   support

    negative     1.0000    1.0000    1.0000       100
     neutral     1.0000    1.0000    1.0000       100
    positive     1.0000    1.0000    1.0000       100

    accuracy                         1.0000       300
   macro avg     1.0000    1.0000    1.0000       300
weighted avg     1.0000    1.0000    1.0000       300