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import torch
from torch.nn.functional import cosine_similarity
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer, AutoModel
import numpy as np
def get_concreteness(prompts, word2score):
scores=[]
for prompt in prompts:
conc_scores=[word2score[w]/10 for w in prompt.split() if w in word2score]
if len(conc_scores) < 1:
scores.append(0.10)
else:
scores.append(np.mean(conc_scores))
return scores
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def compute_cosine_similarity(embeddings_1, embeddings_2):
# Compute cosine similarity between embeddings_1 and embeddings_2
similarities = cosine_similarity(embeddings_1, embeddings_2)
return similarities
class SentenceDataset(Dataset):
def __init__(self, sentences):
self.sentences = sentences
def __len__(self):
return len(self.sentences)
def __getitem__(self, index):
return self.sentences[index]
class Collate_t5:
def __init__(self, tokenizer):
self.t5_tokenizer = tokenizer
def __call__(self, documents):
batch=['summarize: ' + s for s in documents]
# Tokenize sentences
encoded_inputs = self.t5_tokenizer(batch, return_tensors="pt",
add_special_tokens=True, padding='longest',
)
return documents, encoded_inputs
class collate_cl:
def __init__(self, tokenizer):
self.tokenizer = tokenizer
def __call__(self, batch):
# Tokenize sentences
encoded_inputs = self.tokenizer(batch, padding=True, truncation=True, return_tensors='pt')
return encoded_inputs
class mpnet_embed_class():
def __init__(self, device='cuda', nli=True):
self.device = device
if nli:
model = AutoModel.from_pretrained('sentence-transformers/nli-mpnet-base-v2')
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/nli-mpnet-base-v2')
else:
model = AutoModel.from_pretrained('sentence-transformers/all-mpnet-base-v2')
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-mpnet-base-v2')
model.to(device)
self.model = model
self.tokenizer = tokenizer
self.collate_fn = collate_cl(tokenizer)
def get_mpnet_embed_batch(self, predictions, ground_truth, batch_size=10):
dataset_1 = SentenceDataset(predictions)
dataset_2 = SentenceDataset(ground_truth)
dataloader_1 = DataLoader(dataset_1, batch_size=batch_size, collate_fn=self.collate_fn, num_workers=1)
dataloader_2 = DataLoader(dataset_2, batch_size=batch_size, collate_fn=self.collate_fn, num_workers=1)
# Compute token embeddings
embeddings_1 = []
embeddings_2 = []
with torch.no_grad():
for count, (batch_1, batch_2) in enumerate(zip(dataloader_1, dataloader_2)):
if count % 50 == 0:
print(count, ' out of ', len(dataloader_2))
batch_1 = {key: value.to(self.device) for key, value in batch_1.items()}
batch_2 = {key: value.to(self.device) for key, value in batch_2.items()}
model_output_1 = self.model(**batch_1)
model_output_2 = self.model(**batch_2)
sentence_embeddings_1 = mean_pooling(model_output_1, batch_1['attention_mask'])
sentence_embeddings_2 = mean_pooling(model_output_2, batch_2['attention_mask'])
embeddings_1.append(sentence_embeddings_1)
embeddings_2.append(sentence_embeddings_2)
# Concatenate embeddings
embeddings_1 = torch.cat(embeddings_1)
embeddings_2 = torch.cat(embeddings_2)
# Normalize embeddings
embeddings_1 = torch.nn.functional.normalize(embeddings_1, p=2, dim=1)
embeddings_2 = torch.nn.functional.normalize(embeddings_2, p=2, dim=1)
# Compute cosine similarity
similarities = compute_cosine_similarity(embeddings_1, embeddings_2)
# # Average cosine similarity
# average_similarity = torch.mean(similarities)
return similarities
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