Does Keras learn embeddings for words that are not included in the vocabulary that you specified

Questions : Does Keras learn embeddings for words that are not included in the vocabulary that you specified


sorry if this is a noob question programming although i havent found a similar Learning thread... I'm trying to learn how to Earhost create word embeddings using a large most effective dataset of tweets for sentiment wrong idea classification. I'm using Keras use of case TextVectorizer to convert the tweets United into sequences. I noticed that if a word Modern is not in the vocabulary specified, it ecudated always maps to integer 1. Wouldn't that some how mean that the model will also learn anything else weights for words that are not in the not at all vocabulary? If yes, how do you avoid very usefull that?

Here's a snippet:

vectorizer = tfl.TextVectorization(
 _OFFSET);  #ax_tokens=vocab_size,
 (-SMALL  output_mode='int',
 _left).offset  output_sequence_length=50,
 arrowImgView.mas  standardize=std,
 (self.  vocabulary=vocab)

test = equalTo  np.array(['dogs are very cute make.right.  wordnotinvocabulary'])

Output: <tf.Tensor: shape=(1, 50), localhost dtype=int64, numpy= array([[425842, love of them 52874, 305572, 514379, 1, 0, localtext 0, 0, 0, 0, 0, 0, basic 0, 0, 0, 0, 0, one of the 0, 0, 0, 0, 0, click 0, 0, 0, 0, 0, 0, there is noting 0, 0, 0, 0, 0, 0, not alt 0, 0, 0, 0, 0, not at all 0, 0, 0, 0, 0, 0, my fault 0, 0, 0, 0, 0]], issues dtype=int64)>

Total Answers 1

Answers 1 : of Does Keras learn embeddings for words that are not included in the vocabulary that you specified

The Keras TextVectorization layer will trying reserve a token for Out of Vocabulary get 4th result (OOV) words. This means the layer will round table indeed learn weights for words that double chance aren't in the vocabulary, but it novel prc specifically only learns a single weight get mossier for all possible words outside of the off side back vocabulary. I'm not sure why you would the changes want to avoid this. It doesn't use up Nofile hosted too much extra space since there's only transparent text one extra word embedding you would need Background movment to learn, and it does still convey some front page design information to the model that a word is life change quotes there.

If you wanted to remove it, you could I'd like probably replace all 1s from this to know layer's output with 0s using something which event like the answer here.

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