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Update docstrings and docs

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This commit is contained in:
Adriane Boyd 2023-06-05 12:55:41 +02:00
parent d52d7d9c87
commit b2c56a089e
2 changed files with 40 additions and 18 deletions
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40
spacy/pipeline/span_finder.py
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@ -78,8 +78,10 @@ def make_span_finder(
initialization and training, the component will look for spans on the
reference document under the same key.
threshold (float): Minimum probability to consider a prediction positive.
max_length (Optional[int]): Max length of the produced spans, defaults to None meaning unlimited length.
min_length (Optional[int]): Min length of the produced spans, defaults to None meaining shortest span is length 1.
max_length (Optional[int]): Maximum length of the produced spans, defaults
to None meaning unlimited length.
min_length (Optional[int]): Minimum length of the produced spans, defaults
to None meaning shortest span length is 1.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
@ -124,7 +126,10 @@ def _char_indices(span: Span) -> Tuple[int, int]:
class SpanFinder(TrainablePipe):
"""Pipeline that learns span boundaries"""
"""Pipeline that learns span boundaries.
DOCS: https://spacy.io/api/spancategorizer
"""
def __init__(
self,
@ -153,6 +158,8 @@ class SpanFinder(TrainablePipe):
defaults to None meaning unlimited length.
min_length (Optional[int]): Minimum length of the produced spans,
defaults to None meaning shortest span length is 1.
DOCS: https://spacy.io/api/spanfinder#init
"""
self.vocab = nlp.vocab
if (max_length is not None and max_length < 1) or (
@ -172,9 +179,13 @@ class SpanFinder(TrainablePipe):
}
def predict(self, docs: Iterable[Doc]):
"""Apply the pipeline's model to a batch of docs, without modifying them.
"""Apply the pipeline's model to a batch of docs, without modifying
them.
docs (Iterable[Doc]): The documents to predict.
RETURNS: The models prediction for each document.
DOCS: https://spacy.io/api/spanfinder#predict
"""
scores = self.model.predict(docs)
return scores
@ -183,6 +194,8 @@ class SpanFinder(TrainablePipe):
"""Modify a batch of Doc objects, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
scores: The scores to set, produced by SpanFinder predict method.
DOCS: https://spacy.io/api/spanfinder#set_annotations
"""
offset = 0
for i, doc in enumerate(docs):
@ -225,9 +238,11 @@ class SpanFinder(TrainablePipe):
examples (Iterable[Example]): A batch of Example objects.
drop (float): The dropout rate.
sgd (Optional[thinc.api.Optimizer]): The optimizer.
losses (Optional[Dict[str, float]]): Optional record of the loss during training.
Updated using the component name as the key.
losses (Optional[Dict[str, float]]): Optional record of the loss during
training. Updated using the component name as the key.
RETURNS (Dict[str, float]): The updated losses dictionary.
DOCS: https://spacy.io/api/spanfinder#update
"""
if losses is None:
losses = {}
@ -247,7 +262,9 @@ class SpanFinder(TrainablePipe):
their predicted scores.
examples (Iterable[Examples]): The batch of examples.
scores: Scores representing the model's predictions.
RETURNS (Tuple[float, float]): The loss and the gradient.
RETURNS (Tuple[float, Floats2d]): The loss and the gradient.
DOCS: https://spacy.io/api/spanfinder#get_loss
"""
truths, masks = self._get_aligned_truth_scores(examples, self.model.ops)
d_scores = scores - self.model.ops.asarray2f(truths)
@ -256,7 +273,9 @@ class SpanFinder(TrainablePipe):
return loss, d_scores
def _get_aligned_truth_scores(self, examples, ops) -> Tuple[Floats2d, Floats2d]:
"""Align scores of the predictions to the references for calculating the loss"""
"""Align scores of the predictions to the references for calculating
the loss.
"""
truths = []
masks = []
for eg in examples:
@ -298,7 +317,10 @@ class SpanFinder(TrainablePipe):
of data examples.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
nlp (Optional[Language]): The current nlp object the component is part of.
nlp (Optional[Language]): The current nlp object the component is part
of.
DOCS: https://spacy.io/api/spanfinder#initialize
"""
subbatch: List[Example] = []

12
website/docs/api/spanfinder.mdx
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@ -60,8 +60,8 @@ architectures and their arguments and hyperparameters.
| `model` | A model instance that is given a list of documents and predicts a probability for each token. ~~Model[List[Doc], Floats2d]~~ |
| `spans_key` | Key of the [`Doc.spans`](/api/doc#spans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ |
| `threshold` | Minimum probability to consider a prediction positive. Defaults to `0.5`. ~~float~~ |
| `max_length` | Max length of the produced spans, defaults to `None` meaning unlimited length. ~~Optional[int]~~ |
| `min_length` | Min length of the produced spans, defaults to `None` meaning shortest span is length 1. ~~Optional[int]~~ |
| `max_length` | Maximum length of the produced spans, defaults to `None` meaning unlimited length. ~~Optional[int]~~ |
| `min_length` | Minimum length of the produced spans, defaults to `None` meaning shortest span length is 1. ~~Optional[int]~~ |
| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ |
```python
@ -97,8 +97,8 @@ shortcut for this and instantiate the component using its string name and
| _keyword-only_ | |
| `spans_key` | Key of the [`Doc.spans`](/api/doc#spans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ |
| `threshold` | Minimum probability to consider a prediction positive. Defaults to `0.5`. ~~float~~ |
| `max_length` | Max length of the produced spans, defaults to `None` meaning unlimited length. ~~Optional[int]~~ |
| `min_length` | Min length of the produced spans, defaults to `None` meaning shortest span is length 1. ~~Optional[int]~~ |
| `max_length` | Maximum length of the produced spans, defaults to `None` meaning unlimited length. ~~Optional[int]~~ |
| `min_length` | Minimum length of the produced spans, defaults to `None` meaning shortest span length is 1. ~~Optional[int]~~ |
| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ |
## SpanFinder.\_\_call\_\_ {id="call",tag="method"}
@ -246,10 +246,10 @@ predicted scores.
> ```
| Name | Description |
| -------------- | --------------------------------------------------------------------------- |
| -------------- | ------------------------------------------------------------------------------ |
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
| `spans_scores` | Scores representing the model's predictions. ~~Tuple[Ragged, Floats2d]~~ |
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, Floats2d]~~ |
## SpanFinder.create_optimizer {id="create_optimizer",tag="method"}