Getting inconsistent results with Bert #3

scovail · 2020-10-18T19:42:12Z

I've gotten inconsistent results trying to generate sentence vectors using Bert, which is causing the cosine distance calculation to be incorrect. When I make two calls to vectorizer.bert and pass a list each time, then calculate cosine distance for the matching pairs, sentences that are identical are not being identified as such (row 1 of the output). However, when I pass the two identical strings as a list and then compare the vectors that were generated, the results are correct (row 2). In the example below, strings 0, 1, 3, 4, 5 and 6 are identical and should have a cosine distance of 0.

In [9]: import pandas as pd
...: from scipy.spatial.distance import cosine
...: from sent2vec.vectorizer import Vectorizer
...:
...: vectorizer = Vectorizer()
...:
...: df=pd.read_csv('temp2.csv', names=['String A', 'String B'])
...: newa = []
...: newb = []
...:
...: for index, row in df.iterrows():
...: newa.append(row['String A'])
...: newb.append(row['String B'])
...:
...: x=newa[50:60]
...: y=newb[50:60]
...:
...: vectorizer.bert(x)
...: xvex=vectorizer.vectors
...: vectorizer.bert(y)
...: yvex=vectorizer.vectors
...:
...: for i in range(0,10):
...: flag = x[i] == y[i]
...: print('\n({:d}) {:45s} {:45s} {:f}'.format(i, x[i], y[i], cosine(xvex[i], yvex[i])))
...: vectorizer.bert([x[i], y[i]])
...: print('({:d}) {:45s} {:45s} {:4f}'.format(i, x[i], y[i], cosine(vectorizer.vectors[0], vectorizer.vectors[1])))

(0) String A: 401k retirement String B: 401k retirement
(1) String A: 401k retirement accounts String B: 401k retirement accounts
(2) String A: 401k retirement funds String B: 401k retirement plans
(3) String A: 401k retirement investing String B: 401k retirement investing
(4) String A: 401k retirement plan String B: 401k retirement plan
(5) String A: 401k retirement plans String B: 401k retirement plans
(6) String A: 401k retirement savings String B: 401k retirement savings
(7) String A: 401k retirement savings plan String B: 401k plan retirement
(8) String A: 401k retirement savings plan String B: 401k retirement plans
(9) String A: 401k retirement services String B: 401k plan retirement

(0) 401k retirement 401k retirement 0.002897
(0) 401k retirement 401k retirement 0.000000

(1) 401k retirement accounts 401k retirement accounts 0.006706
(1) 401k retirement accounts 401k retirement accounts 0.000000

(2) 401k retirement funds 401k retirement plans 0.012481
(2) 401k retirement funds 401k retirement plans 0.013344

(3) 401k retirement investing 401k retirement investing 0.004481
(3) 401k retirement investing 401k retirement investing 0.000000

(4) 401k retirement plan 401k retirement plan 0.006325
(4) 401k retirement plan 401k retirement plan 0.000000

(5) 401k retirement plans 401k retirement plans 0.005616
(5) 401k retirement plans 401k retirement plans 0.000000

(6) 401k retirement savings 401k retirement savings 0.006093
(6) 401k retirement savings 401k retirement savings 0.000000

(7) 401k retirement savings plan 401k plan retirement 0.013586
(7) 401k retirement savings plan 401k plan retirement 0.023076

(8) 401k retirement savings plan 401k retirement plans 0.008529
(8) 401k retirement savings plan 401k retirement plans 0.017313

(9) 401k retirement services 401k plan retirement 0.017170
(9) 401k retirement services 401k plan retirement 0.014167

pdrm83 · 2020-10-19T23:11:26Z

Thanks for your message. It is hard to find what is going on in your implementation. Especially, since you use a file as input, I can't rerun your code. I have one guess though. The input must be in a form of a list of strings. So, if you want to submit a string it has to be in the form of a list of strings with one string: sentences = ["401k retirement accounts"]

There is no randomization in vectorizer.bert. So, it must return identical responses in each run. I added two more tests to this repo to examine this hypothesis. Hope this helps. I will set up automated tests for this project in the near future. After that, everyone can easily fork the repo and submit a PR if they want to make a progress or find a bug. Thanks!

scovail · 2020-10-20T14:46:32Z

Thanks for your response. Rather than use an input file I set up lists within the code and passed those directly to the vectorizer (see below) with the same results as the first test (identical terms not matching). It appears that the second call to vectorizer.bert with the list y is returning different vectors. Hope this makes sense. Thanks.

In [33]: from scipy.spatial.distance import cosine
...: from sent2vec.vectorizer import Vectorizer
...:
...: vectorizer = Vectorizer()
...:
...: x = ['401k retirement', '401k retirement accounts', '401k retirement funds', '401k retirement investing',
...: '401k retirement plan', '401k retirement plans', '401k retirement savings', '401k retirement savings plan',
...: '401k retirement savings plan', '401k retirement services']
...:
...: y = ['401k retirement', '401k retirement accounts', '401k retirement plans', '401k retirement investing',
...: '401k retirement plan', '401k retirement plans', '401k retirement savings', '401k plan retirement',
...: '401k retirement plans', '401k plan retirement']
...:
...: vectorizer.bert(x)
...: xvex=vectorizer.vectors
...: vectorizer.bert(y)
...: yvex=vectorizer.vectors
...:
...: for i in range(0,10):
...: print('\n({:d}) {:45s} {:45s} {:4f}'.format(i, x[i], y[i], cosine(xvex[i], yvex[i])))
...: vectorizer.bert([x[i], y[i]])
...: print('({:d}) {:45s} {:45s} {:4f}'.format(i, x[i], y[i], cosine(vectorizer.vectors[0], vectorizer.vectors[1])))
...: