TRACE-DeBERTa-v3-base

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("yundog/TRACE-DeBERTa-v3-base")
tokenizer = AutoTokenizer.from_pretrained("yundog/TRACE-DeBERTa-v3-base")

# Predict
text = "Therefore, I conclude that the hypothesis is correct."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.sigmoid(outputs.logits)[0]  # Multi-label classification

# Print results
for label, score in zip(model.config.id2label.values(), predictions):
    print(f"{label}: {score:.3f}")