FAQs on the exercise Entity Recognition with Word Embeddings
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I wasn’t able to pass through this exercise without going to the solution code. Step 2 calls for the word2vec model to be named both word2vec() AND word2vec_model() simultaneously.
Call word2vec() on the string "clothes" and assign the result to category .
Call word2vec_model() on the concatenated form of message_nouns and assign the result to tokens .
When you opt for the solution code, you’ll see that the model in the provided code has been renamed word2vec(). I reset the entire exercise, then used the copy-pasted solution code, and was still unable to validate Step 2.
The whole exercise has not fitting captions in instructions:
word2vec is named word2vec_model in script.py
.similarity takes whole category as argument, not category.text as is stated in the hint
I have a question concerning step 4 of this exercise.
The solution was to simply access the first item in message_nouns. I expected the answer to be instead to select the token with the highest value of similarity and tried the following code (assuming that similarities are ordered from highest to lowest and I could access the token with highest similarity as the first item in the list). But I managed only to access the first letter of the first tuple (“s”), not the first item (“shirts”). Can somebody help?
Thank you!
The wording in this lesson was exceptionally confusing as a whole. A prime example is this step which is very hard to follow. Would have been much easier to bullet point out the edge cases and show the user what the format should look like when written out. Just show us a template example such as current_token category 1.22323 and use the bullet points to detail edge cases