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import os
import sys
cwd = os.getcwd()
module2add = '/'.join(cwd.split("/")[:-1])
sys.path.append(module2add)
from configs.model_config import cfg as model_configs
from transformers import AutoTokenizer, AutoModel
import torch
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)
sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
return sum_embeddings / sum_mask
def make_embeddings(sentence_list, pool_fn):
tokenizer = AutoTokenizer.from_pretrained(model_configs.sent_model_name)
model = AutoModel.from_pretrained(model_configs.sent_model_name)
encoded_input = tokenizer(
sentence_list,
padding=True,
truncation=True,
max_length=model_configs.sent_model_seq_limit,
return_tensors='pt'
)
with torch.no_grad():
embeddings = model(**encoded_input)
attn_mask = encoded_input['attention_mask']
sentence_embeddings = pool_fn(embeddings, attn_mask)
return sentence_embeddings
def test_embedder():
sentences = ['This framework generates embeddings for each input sentence',
'Sentences are passed as a list of string.',
'The quick brown fox jumps over the lazy dog.']
embeddings = make_embeddings(sentences)
print(embeddings.shape)
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