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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from torch.nn.functional import cosine_similarity
from collections import Counter
import numpy as np
from device_manager import DeviceManager
class Gemma2BDependencies:
_instance = None
def __new__(cls):
if cls._instance is None:
cls._instance = super(Gemma2BDependencies, cls).__new__(cls)
cls._instance.tokenizer = AutoTokenizer.from_pretrained(
"google/gemma-2b")
cls._instance.model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b")
cls._instance.device = DeviceManager()
cls._instance.model.to(cls._instance.device)
return cls._instance
def calculate_perplexity(self, text: str):
inputs = self.tokenizer(text, return_tensors="pt",
truncation=True, max_length=1024)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
# Calculate the model's output
with torch.no_grad():
outputs = self.model(**inputs, labels=inputs["input_ids"])
loss = outputs.loss
perplexity = torch.exp(loss)
return perplexity.item()
def calculate_burstiness(self, text: str):
# Tokenize the text using GPT-2 tokenizer
tokens = self.tokenizer.tokenize(text)
# Count token frequencies
frequency_counts = list(Counter(tokens).values())
# Calculate variance and mean of frequencies
variance = np.var(frequency_counts)
mean = np.mean(frequency_counts)
# Compute Variance-to-Mean Ratio (VMR) for burstiness
vmr = variance / mean if mean > 0 else 0
return vmr
def get_embedding(self, text: str):
inputs = self.tokenizer(text, return_tensors="pt",
truncation=True, max_length=1024)
inputs = {k: v.to(self.device) for k, v in inputs.items()}
with torch.no_grad():
outputs = self.model(**inputs, output_hidden_states=True)
last_hidden_states = outputs.hidden_states[-1]
# Average the token embeddings to get a sentence-level embedding
embedding = torch.mean(last_hidden_states, dim=1)
return embedding
def calculate_cosine_similarity(self, question: str, answer: str):
embedding1 = self.get_embedding(question)
embedding2 = self.get_embedding(answer)
# Ensure the embeddings are in the correct shape for cosine_similarity
return cosine_similarity(embedding1, embedding2).item()
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