What is the vector value of [CLS] [SEP] tokens in BERT The 2019 Stack Overflow Developer Survey Results Are InHow much training data does word2vec need?How word2vec can handle unseen / new words to bypass this for new classifications?Word labeling with TensorflowInput for LSTM for financial time series directional predictionwhat actually word embedding dimensions values represent?Keras: apply masking to non-sequential dataWhat kind of neural network would work best for loosely-defined data, like video game RAM?How to dual encode two sentences to show similarity scoreUnderstanding output of LSTM for regression
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What is the vector value of [CLS] [SEP] tokens in BERT
The 2019 Stack Overflow Developer Survey Results Are InHow much training data does word2vec need?How word2vec can handle unseen / new words to bypass this for new classifications?Word labeling with TensorflowInput for LSTM for financial time series directional predictionwhat actually word embedding dimensions values represent?Keras: apply masking to non-sequential dataWhat kind of neural network would work best for loosely-defined data, like video game RAM?How to dual encode two sentences to show similarity scoreUnderstanding output of LSTM for regression
$begingroup$
In BERT, They replace separator and start of sentence with special token labels. What are there corresponding values in embedding_matrix. Are they 0-vector?
I wanted to replace the proper nouns like names, buildings, locations with similar approach. How should i go about masking the same?
lstm word-embeddings
asked Feb 27 at 9:30
ItachiItachi
1713
$endgroup$
add a comment |
$begingroup$
In BERT, They replace separator and start of sentence with special token labels. What are there corresponding values in embedding_matrix. Are they 0-vector?
I wanted to replace the proper nouns like names, buildings, locations with similar approach. How should i go about masking the same?
lstm word-embeddings
asked Feb 27 at 9:30
ItachiItachi
1713
$endgroup$
add a comment |
$begingroup$
In BERT, They replace separator and start of sentence with special token labels. What are there corresponding values in embedding_matrix. Are they 0-vector?
I wanted to replace the proper nouns like names, buildings, locations with similar approach. How should i go about masking the same?
lstm word-embeddings
asked Feb 27 at 9:30
ItachiItachi
1713
$endgroup$
In BERT, They replace separator and start of sentence with special token labels. What are there corresponding values in embedding_matrix. Are they 0-vector?
I wanted to replace the proper nouns like names, buildings, locations with similar approach. How should i go about masking the same?
lstm word-embeddings
lstm word-embeddings
asked Feb 27 at 9:30
ItachiItachi
1713
asked Feb 27 at 9:30
ItachiItachi
1713
asked Feb 27 at 9:30
ItachiItachi
1713
asked Feb 27 at 9:30
ItachiItachi
1713
asked Feb 27 at 9:30
ItachiItachi
1713
1713
add a comment |
add a comment |
1 Answer
1
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oldest
votes
$begingroup$
I think replacing Proper Nouns with an aggregated Proper Nouns vector should do the trick
Basically, words like Barcelona, Spain, India, and other locations will have a high similarity to a bias vector, we can find out that vector using standard deviation of this matrix along the column axis. Whichever has a low value can be retained, rest all can be set to 0
eg: Delhi, can be replace with [2,3,4,0,0,0...] where [2,3,4...] are common attributes of other locations
answered Feb 28 at 9:10
ItachiItachi
1713
$endgroup$
add a comment |
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1 Answer
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active
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1 Answer
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active
oldest
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$begingroup$
I think replacing Proper Nouns with an aggregated Proper Nouns vector should do the trick
Basically, words like Barcelona, Spain, India, and other locations will have a high similarity to a bias vector, we can find out that vector using standard deviation of this matrix along the column axis. Whichever has a low value can be retained, rest all can be set to 0
eg: Delhi, can be replace with [2,3,4,0,0,0...] where [2,3,4...] are common attributes of other locations
answered Feb 28 at 9:10
ItachiItachi
1713
$endgroup$
add a comment |
$begingroup$
I think replacing Proper Nouns with an aggregated Proper Nouns vector should do the trick
Basically, words like Barcelona, Spain, India, and other locations will have a high similarity to a bias vector, we can find out that vector using standard deviation of this matrix along the column axis. Whichever has a low value can be retained, rest all can be set to 0
eg: Delhi, can be replace with [2,3,4,0,0,0...] where [2,3,4...] are common attributes of other locations
answered Feb 28 at 9:10
ItachiItachi
1713
$endgroup$
add a comment |
$begingroup$
I think replacing Proper Nouns with an aggregated Proper Nouns vector should do the trick
Basically, words like Barcelona, Spain, India, and other locations will have a high similarity to a bias vector, we can find out that vector using standard deviation of this matrix along the column axis. Whichever has a low value can be retained, rest all can be set to 0
eg: Delhi, can be replace with [2,3,4,0,0,0...] where [2,3,4...] are common attributes of other locations
answered Feb 28 at 9:10
ItachiItachi
1713
$endgroup$
I think replacing Proper Nouns with an aggregated Proper Nouns vector should do the trick
Basically, words like Barcelona, Spain, India, and other locations will have a high similarity to a bias vector, we can find out that vector using standard deviation of this matrix along the column axis. Whichever has a low value can be retained, rest all can be set to 0
eg: Delhi, can be replace with [2,3,4,0,0,0...] where [2,3,4...] are common attributes of other locations
answered Feb 28 at 9:10
ItachiItachi
1713
answered Feb 28 at 9:10
ItachiItachi
1713
answered Feb 28 at 9:10
ItachiItachi
1713
answered Feb 28 at 9:10
ItachiItachi
1713
1713
add a comment |
add a comment |
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