mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-30 20:06:30 +03:00
103 lines
4.3 KiB
Markdown
103 lines
4.3 KiB
Markdown
|
import Infobox from 'components/infobox'
|
|||
|
|
|
|||
|
|
Similarity is determined by comparing **word vectors** or "word embeddings",
|
|||
|
|
multi-dimensional meaning representations of a word. Word vectors can be
|
|||
|
|
generated using an algorithm like
|
|||
|
|
[word2vec](https://en.wikipedia.org/wiki/Word2vec) and usually look like this:
|
|||
|
|
|
|||
|
|
```python
|
|||
|
|
### banana.vector
|
|||
|
|
array([2.02280000e-01, -7.66180009e-02, 3.70319992e-01,
|
|||
|
|
3.28450017e-02, -4.19569999e-01, 7.20689967e-02,
|
|||
|
|
-3.74760002e-01, 5.74599989e-02, -1.24009997e-02,
|
|||
|
|
5.29489994e-01, -5.23800015e-01, -1.97710007e-01,
|
|||
|
|
-3.41470003e-01, 5.33169985e-01, -2.53309999e-02,
|
|||
|
|
1.73800007e-01, 1.67720005e-01, 8.39839995e-01,
|
|||
|
|
5.51070012e-02, 1.05470002e-01, 3.78719985e-01,
|
|||
|
|
2.42750004e-01, 1.47449998e-02, 5.59509993e-01,
|
|||
|
|
1.25210002e-01, -6.75960004e-01, 3.58420014e-01,
|
|||
|
|
# ... and so on ...
|
|||
|
|
3.66849989e-01, 2.52470002e-03, -6.40089989e-01,
|
|||
|
|
-2.97650009e-01, 7.89430022e-01, 3.31680000e-01,
|
|||
|
|
-1.19659996e+00, -4.71559986e-02, 5.31750023e-01], dtype=float32)
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
<Infobox title="Important note" variant="warning">
|
|||
|
|
|
|||
|
|
To make them compact and fast, spaCy's small [models](/models) (all packages
|
|||
|
|
that end in `sm`) **don't ship with word vectors**, and only include
|
|||
|
|
context-sensitive **tensors**. This means you can still use the `similarity()`
|
|||
|
|
methods to compare documents, spans and tokens – but the result won't be as
|
|||
|
|
good, and individual tokens won't have any vectors assigned. So in order to use
|
|||
|
|
_real_ word vectors, you need to download a larger model:
|
|||
|
|
|
|||
|
|
```diff
|
|||
|
|
- python -m spacy download en_core_web_sm
|
|||
|
|
+ python -m spacy download en_core_web_lg
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
</Infobox>
|
|||
|
|
|
|||
|
|
Models that come with built-in word vectors make them available as the
|
|||
|
|
[`Token.vector`](/api/token#vector) attribute. [`Doc.vector`](/api/doc#vector)
|
|||
|
|
and [`Span.vector`](/api/span#vector) will default to an average of their token
|
|||
|
|
vectors. You can also check if a token has a vector assigned, and get the L2
|
|||
|
|
norm, which can be used to normalize vectors.
|
|||
|
|
|
|||
|
|
```python
|
|||
|
|
### {executable="true"}
|
|||
|
|
import spacy
|
|||
|
|
|
|||
|
|
nlp = spacy.load('en_core_web_md')
|
|||
|
|
tokens = nlp(u'dog cat banana afskfsd')
|
|||
|
|
|
|||
|
|
for token in tokens:
|
|||
|
|
print(token.text, token.has_vector, token.vector_norm, token.is_oov)
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
> - **Text**: The original token text.
|
|||
|
|
> - **has vector**: Does the token have a vector representation?
|
|||
|
|
> - **Vector norm**: The L2 norm of the token's vector (the square root of the
|
|||
|
|
> sum of the values squared)
|
|||
|
|
> - **OOV**: Out-of-vocabulary
|
|||
|
|
|
|||
|
|
The words "dog", "cat" and "banana" are all pretty common in English, so they're
|
|||
|
|
part of the model's vocabulary, and come with a vector. The word "afskfsd" on
|
|||
|
|
the other hand is a lot less common and out-of-vocabulary – so its vector
|
|||
|
|
representation consists of 300 dimensions of `0`, which means it's practically
|
|||
|
|
nonexistent. If your application will benefit from a **large vocabulary** with
|
|||
|
|
more vectors, you should consider using one of the larger models or loading in a
|
|||
|
|
full vector package, for example,
|
|||
|
|
[`en_vectors_web_lg`](/models/en#en_vectors_web_lg), which includes over **1
|
|||
|
|
million unique vectors**.
|
|||
|
|
|
|||
|
|
spaCy is able to compare two objects, and make a prediction of **how similar
|
|||
|
|
they are**. Predicting similarity is useful for building recommendation systems
|
|||
|
|
or flagging duplicates. For example, you can suggest a user content that's
|
|||
|
|
similar to what they're currently looking at, or label a support ticket as a
|
|||
|
|
duplicate if it's very similar to an already existing one.
|
|||
|
|
|
|||
|
|
Each `Doc`, `Span` and `Token` comes with a
|
|||
|
|
[`.similarity()`](/api/token#similarity) method that lets you compare it with
|
|||
|
|
another object, and determine the similarity. Of course similarity is always
|
|||
|
|
subjective – whether "dog" and "cat" are similar really depends on how you're
|
|||
|
|
looking at it. spaCy's similarity model usually assumes a pretty general-purpose
|
|||
|
|
definition of similarity.
|
|||
|
|
|
|||
|
|
```python
|
|||
|
|
### {executable="true"}
|
|||
|
|
import spacy
|
|||
|
|
|
|||
|
|
nlp = spacy.load('en_core_web_md') # make sure to use larger model!
|
|||
|
|
tokens = nlp(u'dog cat banana')
|
|||
|
|
|
|||
|
|
for token1 in tokens:
|
|||
|
|
for token2 in tokens:
|
|||
|
|
print(token1.text, token2.text, token1.similarity(token2))
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
In this case, the model's predictions are pretty on point. A dog is very similar
|
|||
|
|
to a cat, whereas a banana is not very similar to either of them. Identical
|
|||
|
|
tokens are obviously 100% similar to each other (just not always exactly `1.0`,
|
|||
|
|
because of vector math and floating point imprecisions).
|