<<<<<<< HEAD from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained("thethinkmachine/Maxwell-Task-Complexity-Scorer-v0.2") model = AutoModelForSequenceClassification.from_pretrained("thethinkmachine/Maxwell-Task-Complexity-Scorer-v0.2") # Example task task_description = "find a new theory" # Tokenize the input inputs = tokenizer(task_description, return_tensors="pt") # Perform inference with torch.no_grad(): outputs = model(**inputs) complexity_score = torch.sigmoid(outputs.logits).item() print(f"Task Complexity Score: {complexity_score:.4f}") ======= from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch # Load the tokenizer and model tokenizer = AutoTokenizer.from_pretrained("thethinkmachine/Maxwell-Task-Complexity-Scorer-v0.2") model = AutoModelForSequenceClassification.from_pretrained("thethinkmachine/Maxwell-Task-Complexity-Scorer-v0.2") # Example task task_description = "find a new theory" # Tokenize the input inputs = tokenizer(task_description, return_tensors="pt") # Perform inference with torch.no_grad(): outputs = model(**inputs) complexity_score = torch.sigmoid(outputs.logits).item() print(f"Task Complexity Score: {complexity_score:.4f}") >>>>>>> b1313c5d084e410cadf261f2fafd8929cb149a4f