-
```
On Distributions, for open text, create a list of most common words in
descending order. Example: [answer 1, answer 2] => bag of words => [ [word1,
count], [word2, count] ]. sortDescending => Li…
-
```
On Distributions, for open text, create a list of most common words in
descending order. Example: [answer 1, answer 2] => bag of words => [ [word1,
count], [word2, count] ]. sortDescending => Li…
-
```
On Distributions, for open text, create a list of most common words in
descending order. Example: [answer 1, answer 2] => bag of words => [ [word1,
count], [word2, count] ]. sortDescending => Li…
-
* Use pretrained models such as: https://ai.googleblog.com/2018/11/open-sourcing-bert-state-of-art-pre.html
* Use simple text representation techniques such as bag-of-words
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I see word embeddings as some potentially low hanging fruit for a more robust product. Namely, word embeddings, such as GloVe, are (A) additive and (B) can quantify the similarity between words/phrase…
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Bag of Words + doc term matrix + tf-idf ... to be implemented to AI.
See also TensorFlow word2vec. RNN (https://machinelearnings.co/tensorflow-text-classification-615198df9231).
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Thanks for providing such wonderful work. We are curious about how to conduct bag of words training and can you provide training details?
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If I'd like to use CMG on my own dataset (for video and audio), how should I prepare the data? I've got video-audio pairs, whether should I extract their features? If yes, what feature extraction mode…
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**Describe the bug**
We have a cluster consisting of 3 nodes, and I have been running the same command on each node. However, I have noticed that I am getting different scores as results. Could you …
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I am using your code for my dataset. Each article is now represented with a bag-of-words histogram
vector. What does this next step mean?
normalized over the maximum occurrences of each word in all…