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pdrm83 / sent2vec

How to encode sentences in a high-dimensional vector space, a.k.a., sentence embedding.
MIT License
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Same sentences yet different vectors #18

Open raza-sikander opened 2 years ago

raza-sikander commented 2 years ago

Hi i have a situation where i have a mongodb from which im getting sentences and saving into list. After that i have a sentence which exists in the list. but when i create vectors different vectors seemed to be created and cosine distance also changes as the vectors are different where the cosine value should be 0 but getting 0.22

from scipy import spatial
from sent2vec.vectorizer import Vectorizer
from pymongo import MongoClient
# Connecting to local mongo
client = MongoClient('localhost', 27017)
database = client['test']
collection = database['test']
cursor = collection.find({})
#empty list to append the data from mongo
info = []
#test data
info1 = ['hello im john',]
info2 = ['hello im john',]
for document in cursor:
    info.append(str(document["title"]))
    # break
print(info) #check the data
print(info1)
vectorizer = Vectorizer()
vectorizer.run(info)
vectors_info = vectorizer.vectors #mongo data vectors
vectorizer = Vectorizer()
vectorizer.run(info1)
vectors_info1 = vectorizer.vectors  #test data vectors

#comparing the vectors
print(vectors_info[0]==vectors_info1[0])

vectorizer = Vectorizer()
vectorizer.run(info2)
vectors_info2 = vectorizer.vectors 
#comparing same text again
print(vectors_info1[0]==vectors_info2[0])

output : 

 python3 test.py 
['hello im john', 'hello im tez']
['hello im john']
Initializing Bert distilbert-base-uncased
Vectorization done on cpu
Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.bias']
- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Initializing Bert distilbert-base-uncased
Vectorization done on cpu
Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.bias']
- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
[False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False
 False False False False False False False False False False False False]
Initializing Bert distilbert-base-uncased
Vectorization done on cpu
Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_transform.weight', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.bias']
- This IS expected if you are initializing DistilBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
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