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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 #### Disabling formatting
There are a few cases where auto-formatting doesn't improve readability – for 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. 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 Wrapping a block in `# fmt: off` and `# fmt: on` lets you disable formatting
for that particular code. Here's an example: 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 | | Name | Type | Description |
| --------------------------- | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | | -------------------- | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. | | `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. | | `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. | | `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. | | `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. | | `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. | | `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 | | `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. |
| `begin_training` is called. |
### spacy.TextCatCNN.v1 {#TextCatCNN} ### 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. architecture is usually less accurate than the ensemble, but runs faster.
| Name | Type | Description | | Name | Type | Description |
| ------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | ------------------- | ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. | | `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. | | `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} ### 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. others, but may not be as accurate, especially if texts are short.
| Name | Type | Description | | Name | Type | Description |
| ------------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | ------------------- | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. | | `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. | | `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_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"} ## 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 Compared to regular install via pip, the
[`requirements.txt`](https://github.com/explosion/spaCy/tree/master/requirements.txt) [`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 [quickstart widget](#quickstart) to get the right commands for your platform and
Python version. 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 After installing the package, the custom colors will be used when visualizing
visualizing text with `displacy`. Whenever the label `SNEK` is assigned, it will text with `displacy`. Whenever the label `SNEK` is assigned, it will be
be displayed in `#3dff74`. displayed in `#3dff74`.
import DisplaCyEntSnekHtml from 'images/displacy-ent-snek.html' 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, # With additional functionality: in/not in, replace, pprint, round, + for lists,
# rendering empty dicts # rendering empty dicts
# This script is mostly used to generate the JavaScript function for the # This script is mostly used to generate the JavaScript function for the
# training quicktart widget. # training quickstart widget.
import contextlib import contextlib
import json import json
import re import re