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fix typos

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Alex Strick van Linschoten 2024-04-28 12:23:40 +02:00
parent d65e3c31a6
commit d314bbb0f4
15 changed files with 19 additions and 19 deletions
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4
spacy/cli/find_threshold.py
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@ -39,7 +39,7 @@ def find_threshold_cli(
# fmt: on
):
"""
Runs prediction trials for a trained model with varying tresholds to maximize
Runs prediction trials for a trained model with varying thresholds to maximize
the specified metric. The search space for the threshold is traversed linearly
from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout`
(the corresponding API call to `spacy.cli.find_threshold.find_threshold()`
@ -81,7 +81,7 @@ def find_threshold(
silent: bool = True,
) -> Tuple[float, float, Dict[float, float]]:
"""
Runs prediction trials for models with varying tresholds to maximize the specified metric.
Runs prediction trials for models with varying thresholds to maximize the specified metric.
model (Union[str, Path]): Pipeline to evaluate. Can be a package or a path to a data directory.
data_path (Path): Path to file with DocBin with docs to use for threshold search.
pipe_name (str): Name of pipe to examine thresholds for.

2
spacy/tests/test_language.py
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@ -327,7 +327,7 @@ def test_language_pipe_error_handler(n_process):
nlp.set_error_handler(raise_error)
with pytest.raises(ValueError):
list(nlp.pipe(texts, n_process=n_process))
# set explicitely to ignoring
# set explicitly to ignoring
nlp.set_error_handler(ignore_error)
docs = list(nlp.pipe(texts, n_process=n_process))
assert len(docs) == 0

2
website/docs/api/attributes.mdx
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@ -49,7 +49,7 @@ appending `_` as in `token.dep_`.
| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `DEP` | The token's dependency label. ~~str~~ |
| `ENT_ID` | The token's entity ID (`ent_id`). ~~str~~ |
| `ENT_IOB` | The IOB part of the token's entity tag. Uses custom integer vaues rather than the string store: unset is `0`, `I` is `1`, `O` is `2`, and `B` is `3`. ~~str~~ |
| `ENT_IOB` | The IOB part of the token's entity tag. Uses custom integer values rather than the string store: unset is `0`, `I` is `1`, `O` is `2`, and `B` is `3`. ~~str~~ |
| `ENT_KB_ID` | The token's entity knowledge base ID. ~~str~~ |
| `ENT_TYPE` | The token's entity label. ~~str~~ |
| `IS_ALPHA` | Token text consists of alphabetic characters. ~~bool~~ |

4
website/docs/api/cli.mdx
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@ -521,7 +521,7 @@ New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
'LOC' (2113), 'TIME' (1616), 'WORK_OF_ART' (1229), 'QUANTITY' (1150), 'FAC'
(1134), 'EVENT' (974), 'PRODUCT' (935), 'LAW' (444), 'LANGUAGE' (338)
✔ Good amount of examples for all labels
✔ Examples without occurences available for all labels
✔ Examples without occurrences available for all labels
✔ No entities consisting of or starting/ending with whitespace
=========================== Part-of-speech Tagging ===========================
@ -1233,7 +1233,7 @@ $ python -m spacy apply [model] [data-path] [output-file] [--code] [--text-key]
## find-threshold {id="find-threshold",version="3.5",tag="command"}
Runs prediction trials for a trained model with varying tresholds to maximize
Runs prediction trials for a trained model with varying thresholds to maximize
the specified metric. The search space for the threshold is traversed linearly
from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout`
(the corresponding API call to `spacy.cli.find_threshold.find_threshold()`

4
website/docs/api/entitylinker.mdx
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@ -67,7 +67,7 @@ architectures and their arguments and hyperparameters.
| `generate_empty_kb` <Tag variant="new">3.5.1</Tag> | Function that generates an empty `KnowledgeBase` object. Defaults to [`spacy.EmptyKB.v2`](/api/architectures#EmptyKB), which generates an empty [`InMemoryLookupKB`](/api/inmemorylookupkb). ~~Callable[[Vocab, int], KnowledgeBase]~~ |
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the threshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
```python
%%GITHUB_SPACY/spacy/pipeline/entity_linker.py
@ -114,7 +114,7 @@ custom knowledge base, you should either call
| `incl_context` | Whether or not to include the local context in the model. ~~bool~~ |
| `overwrite` <Tag variant="new">3.2</Tag> | Whether existing annotation is overwritten. Defaults to `True`. ~~bool~~ |
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_links`](/api/scorer#score_links). ~~Optional[Callable]~~ |
| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the treshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
| `threshold` <Tag variant="new">3.4</Tag> | Confidence threshold for entity predictions. The default of `None` implies that all predictions are accepted, otherwise those with a score beneath the threshold are discarded. If there are no predictions with scores above the threshold, the linked entity is `NIL`. ~~Optional[float]~~ |
## EntityLinker.\_\_call\_\_ {id="call",tag="method"}

2
website/docs/api/entityruler.mdx
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@ -173,7 +173,7 @@ happens automatically after the component has been added to the pipeline using
[`nlp.add_pipe`](/api/language#add_pipe). If the entity ruler was initialized
with `overwrite_ents=True`, existing entities will be replaced if they overlap
with the matches. When matches overlap in a Doc, the entity ruler prioritizes
longer patterns over shorter, and if equal the match occuring first in the Doc
longer patterns over shorter, and if equal the match occurring first in the Doc
is chosen.
> #### Example

2
website/docs/api/span.mdx
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@ -288,7 +288,7 @@ it – so no NP-level coordination, no prepositional phrases, and no relative
clauses.
If the `noun_chunk` [syntax iterator](/usage/linguistic-features#language-data)
has not been implemeted for the given language, a `NotImplementedError` is
has not been implemented for the given language, a `NotImplementedError` is
raised.
> #### Example

2
website/docs/api/transformer.mdx
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@ -405,7 +405,7 @@ by this class. Instances of this class are typically assigned to the
| `align` | Alignment from the `Doc`'s tokenization to the wordpieces. This is a ragged array, where `align.lengths[i]` indicates the number of wordpiece tokens that token `i` aligns against. The actual indices are provided at `align[i].dataXd`. ~~Ragged~~ |
| `width` | The width of the last hidden layer. ~~int~~ |
### TransformerData.empty {id="transformerdata-emoty",tag="classmethod"}
### TransformerData.empty {id="transformerdata-empty",tag="classmethod"}
Create an empty `TransformerData` container.

2
website/docs/api/vectors.mdx
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@ -440,7 +440,7 @@ Load state from a binary string.
> #### Example
>
> ```python
> fron spacy.vectors import Vectors
> from spacy.vectors import Vectors
> vectors_bytes = vectors.to_bytes()
> new_vectors = Vectors(StringStore())
> new_vectors.from_bytes(vectors_bytes)

2
website/docs/usage/layers-architectures.mdx
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@ -830,7 +830,7 @@ retrieve and add to them.
After creation, the component needs to be
[initialized](/usage/training#initialization). This method can define the
relevant labels in two ways: explicitely by setting the `labels` argument in the
relevant labels in two ways: explicitly by setting the `labels` argument in the
[`initialize` block](/api/data-formats#config-initialize) of the config, or
implicately by deducing them from the `get_examples` callback that generates the
full **training data set**, or a representative sample.

2
website/docs/usage/linguistic-features.mdx
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@ -1900,7 +1900,7 @@ the two words.
"Shore": ("coast", 0.732257),
"Precautionary": ("caution", 0.490973),
"hopelessness": ("sadness", 0.742366),
"Continous": ("continuous", 0.732549),
"Continuous": ("continuous", 0.732549),
"Disemboweled": ("corpse", 0.499432),
"biostatistician": ("scientist", 0.339724),
"somewheres": ("somewheres", 0.402736),

4
website/docs/usage/projects.mdx
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@ -173,7 +173,7 @@ detected, a corresponding warning is displayed. If you'd like to disable the
dependency check, set `check_requirements: false` in your project's
`project.yml`.
### 4. Run a workflow {id="run-workfow"}
### 4. Run a workflow {id="run-workflow"}
> #### project.yml
>
@ -286,7 +286,7 @@ pipelines.
| --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `title` | An optional project title used in `--help` message and [auto-generated docs](#custom-docs). |
| `description` | An optional project description used in [auto-generated docs](#custom-docs). |
| `vars` | A dictionary of variables that can be referenced in paths, URLs and scripts and overriden on the CLI, just like [`config.cfg` variables](/usage/training#config-interpolation). For example, `${vars.name}` will use the value of the variable `name`. Variables need to be defined in the section `vars`, but can be a nested dict, so you're able to reference `${vars.model.name}`. |
| `vars` | A dictionary of variables that can be referenced in paths, URLs and scripts and overridden on the CLI, just like [`config.cfg` variables](/usage/training#config-interpolation). For example, `${vars.name}` will use the value of the variable `name`. Variables need to be defined in the section `vars`, but can be a nested dict, so you're able to reference `${vars.model.name}`. |
| `env` | A dictionary of variables, mapped to the names of environment variables that will be read in when running the project. For example, `${env.name}` will use the value of the environment variable defined as `name`. |
| `directories` | An optional list of [directories](#project-files) that should be created in the project for assets, training outputs, metrics etc. spaCy will make sure that these directories always exist. |
| `assets` | A list of assets that can be fetched with the [`project assets`](/api/cli#project-assets) command. `url` defines a URL or local path, `dest` is the destination file relative to the project directory, and an optional `checksum` ensures that an error is raised if the file's checksum doesn't match. Instead of `url`, you can also provide a `git` block with the keys `repo`, `branch` and `path`, to download from a Git repo. |

2
website/docs/usage/saving-loading.mdx
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@ -346,7 +346,7 @@ them**!
To stick with the theme of
[this entry points blog post](https://amir.rachum.com/blog/2017/07/28/python-entry-points/),
consider the following custom spaCy
[pipeline component](/usage/processing-pipelines#custom-coponents) that prints a
[pipeline component](/usage/processing-pipelines#custom-components) that prints a
snake when it's called:
> #### Package directory structure

2
website/docs/usage/v2-2.mdx
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@ -185,7 +185,7 @@ New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
'LOC' (2113), 'TIME' (1616), 'WORK_OF_ART' (1229), 'QUANTITY' (1150), 'FAC'
(1134), 'EVENT' (974), 'PRODUCT' (935), 'LAW' (444), 'LANGUAGE' (338)
✔ Good amount of examples for all labels
✔ Examples without occurences available for all labels
✔ Examples without occurrences available for all labels
✔ No entities consisting of or starting/ending with whitespace
=========================== Part-of-speech Tagging ===========================

2
website/docs/usage/v3-2.mdx
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@ -138,7 +138,7 @@ backwards compatibility, the tuple format remains available under
`TransformerData.tensors` and `FullTransformerBatch.tensors`. See more details
in the [transformer API docs](/api/architectures#TransformerModel).
`spacy-transfomers` v1.1 also adds support for `transformer_config` settings
`spacy-transformers` v1.1 also adds support for `transformer_config` settings
such as `output_attentions`. Additional output is stored under
`TransformerData.model_output`. More details are in the
[TransformerModel docs](/api/architectures#TransformerModel). The training speed