Created
June 13, 2023 17:41
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from transformers import AutoTokenizer, AutoModel | |
import torch | |
import torch.nn.functional as F | |
#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) | |
# Sentences we want sentence embeddings for | |
sentences = ['This is an example sentence', 'Each sentence is converted'] | |
# Load model from HuggingFace Hub | |
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') | |
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2') | |
# Tokenize sentences | |
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') | |
# Compute token embeddings | |
with torch.no_grad(): | |
model_output = model(**encoded_input) | |
# Perform pooling | |
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) | |
# Normalize embeddings | |
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) | |
print("Sentence embeddings:") | |
print(sentence_embeddings) |
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