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fix typos

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svlandeg 2020-08-17 14:05:48 +02:00
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2
CONTRIBUTING.md
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@ -216,7 +216,7 @@ list of available editor integrations.
#### Disabling formatting
There are a few cases where auto-formatting doesn't improve readability – for
example, in some of the the language data files like the `tag_map.py`, or in
example, in some of the language data files like the `tag_map.py`, or in
the tests that construct `Doc` objects from lists of words and other labels.
Wrapping a block in `# fmt: off` and `# fmt: on` lets you disable formatting
for that particular code. Here's an example:

13
website/docs/api/architectures.md
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@ -490,7 +490,7 @@ network has an internal CNN Tok2Vec layer and uses attention.
> ```
| Name | Type | Description |
| --------------------------- | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| -------------------- | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
| `pretrained_vectors` | bool | Whether or not pretrained vectors will be used in addition to the feature vectors. |
| `width` | int | Output dimension of the feature encoding step. |
@ -499,8 +499,7 @@ network has an internal CNN Tok2Vec layer and uses attention.
| `window_size` | int | The number of contextual vectors to [concatenate](https://thinc.ai/docs/api-layers#expand_window) from the left and from the right. |
| `ngram_size` | int | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3`would give unigram, trigram and bigram features. |
| `dropout` | float | The dropout rate. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the the [`TextCategorizer`](/api/textcategorizer) component will set it when |
| `begin_training` is called. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. |
### spacy.TextCatCNN.v1 {#TextCatCNN}
@ -528,10 +527,10 @@ vectors are mean pooled and used as features in a feed-forward network. This
architecture is usually less accurate than the ensemble, but runs faster.
| Name | Type | Description |
| ------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| ------------------- | ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | The [`tok2vec`](#tok2vec) layer of the model. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. |
### spacy.TextCatBOW.v1 {#TextCatBOW}
@ -550,11 +549,11 @@ An ngram "bag-of-words" model. This architecture should run much faster than the
others, but may not be as accurate, especially if texts are short.
| Name | Type | Description |
| ------------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| ------------------- | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
| `ngram_size` | int | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3`would give unigram, trigram and bigram features. |
| `no_output_layer` | float | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes=True`, else `Logistic`. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. |
## Entity linking architectures {#entitylinker source="spacy/ml/models/entity_linker.py"}

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website/docs/usage/index.md
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@ -169,7 +169,7 @@ python setup.py build_ext --inplace # compile spaCy
Compared to regular install via pip, the
[`requirements.txt`](https://github.com/explosion/spaCy/tree/master/requirements.txt)
additionally installs developer dependencies such as Cython. See the the
additionally installs developer dependencies such as Cython. See the
[quickstart widget](#quickstart) to get the right commands for your platform and
Python version.

6
website/docs/usage/saving-loading.md
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@ -551,9 +551,9 @@ setup(
)
```
After installing the package, the the custom colors will be used when
visualizing text with `displacy`. Whenever the label `SNEK` is assigned, it will
be displayed in `#3dff74`.
After installing the package, the custom colors will be used when visualizing
text with `displacy`. Whenever the label `SNEK` is assigned, it will be
displayed in `#3dff74`.
import DisplaCyEntSnekHtml from 'images/displacy-ent-snek.html'

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website/setup/jinja_to_js.py
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@ -2,7 +2,7 @@
# With additional functionality: in/not in, replace, pprint, round, + for lists,
# rendering empty dicts
# This script is mostly used to generate the JavaScript function for the
# training quicktart widget.
# training quickstart widget.
import contextlib
import json
import re