13.5 One word in different neighbours

Content and key concepts

On the previous page, the drawing shows only part of the space.
The line of the original dictionary is the same. The new view depends on the surrounding entrances and is not recorded back into the weight for each request.
Table E stores the coordinate line for each ID. These coordinates are taught. The familiar cosine is now used for the specific task of finding close representations.
Для изменения рисунка включитеJavaScript; расчёт и теория доступны далее.
One row E, different processing results
Embeddedding lokup depends on ID: the same word selects one line E with fixed weights. But after mixing with neighbors, represence depends on the context. The designation h_i\(l) indicates the position; this is the action of the current passage, not the new line of the learning dictionary.
Experience compares the sequence of cat fish eats and cat bone eats. The cat stands in position, the E[cott] is the same. Causal application with fixed identity projects mixes this line with an accessible prefix. The value of the adjacent line changes, so the contextual output can change without changing the parameters.
It's a manual case.
If the scores of the two available entries are equal, co-officents[.5,.5]. For valutes[1,0.0] and [0.0.1] mixture[.5.0..5]. Replace the second value with [0.0.0], we get[.5,5.0.] The same weights mixing, the other entrance with another exit.
Boundary.
The change in activation is not learning. In this model, the current can be part of the word, and the phrase "contextual representation of the words" then requires agreement on several token positions. Our toy explains the mechanism, does not test the quality of semantic disambigation.
Now let's solve the calculation of staff rather than being assigned by hand:
attention
.
a,b=[1,0,0],[0,0,1]
mixed=[.5*x+.5*y for x,y in zip(a,b)]
assert mixed==[.5,0,.5]
The code repeats the operations just taken care of.
Completely checked Python-example Chapter
Available with a separate file; the short piece shown does not replace its implementation.
The context changes the calculation with fixed parameters.
How can one to get useful information from others?
Python examples of Chapter 13
Academic base