deberta-v3-small-cefr-lora-merged

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

tokenizer = AutoTokenizer.from_pretrained("oguzhandurmaz/deberta-v3-small-cefr-lora-merged")
model = AutoModelForSequenceClassification.from_pretrained("oguzhandurmaz/deberta-v3-small-cefr-lora-merged")

text = "The economic infrastructure is undergoing a significant transformation."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    logits = model(**inputs).logits
    probs = F.softmax(logits, dim=-1)
    predicted_class = torch.argmax(probs, dim=-1).item()

labels = ['A1', 'A2', 'B1', 'B2', 'C1', 'C2']
print(f"Predicted CEFR Level: {labels[predicted_class]}")