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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}")
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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}")
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