* Update processing-pipelines.md
Under "things to try," inform users they can save metadata when using nlp.pipe(foobar, as_tuples=True)
Link to a new example on the attributes page detailing the following:
> ```
> data = [
> ("Some text to process", {"meta": "foo"}),
> ("And more text...", {"meta": "bar"})
> ]
>
> for doc, context in nlp.pipe(data, as_tuples=True):
> # Let's assume you have a "meta" extension registered on the Doc
> doc._.meta = context["meta"]
> ```
from https://stackoverflow.com/questions/57058798/make-spacy-nlp-pipe-process-tuples-of-text-and-additional-information-to-add-as
* Updating the attributes section
Update the attributes section with example of how extensions can be used to store metadata.
* Update processing-pipelines.md
* Update processing-pipelines.md
Made as_tuples example executable and relocated to the end of the "Processing Text" section.
* Update processing-pipelines.md
* Update processing-pipelines.md
Removed extra line
* Reformat and rephrase
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update processing-pipelines.md
Under "things to try," inform users they can save metadata when using nlp.pipe(foobar, as_tuples=True)
Link to a new example on the attributes page detailing the following:
> ```
> data = [
> ("Some text to process", {"meta": "foo"}),
> ("And more text...", {"meta": "bar"})
> ]
>
> for doc, context in nlp.pipe(data, as_tuples=True):
> # Let's assume you have a "meta" extension registered on the Doc
> doc._.meta = context["meta"]
> ```
from https://stackoverflow.com/questions/57058798/make-spacy-nlp-pipe-process-tuples-of-text-and-additional-information-to-add-as
* Updating the attributes section
Update the attributes section with example of how extensions can be used to store metadata.
* Update processing-pipelines.md
* Update processing-pipelines.md
Made as_tuples example executable and relocated to the end of the "Processing Text" section.
* Update processing-pipelines.md
* Update processing-pipelines.md
Removed extra line
* Reformat and rephrase
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update Tokenizer.explain with special matches
Update `Tokenizer.explain` and the pseudo-code in the docs to include
the processing of special cases that contain affixes or whitespace.
* Handle optional settings in explain
* Add test for special matches in explain
Add test for `Tokenizer.explain` for special cases containing affixes.
* Set catalogue lower pin to v2.0.2
* Update importlib-metadata pins to match
* Require catalogue v2.0.3
Switch to vendored `importlib-metadata` v3.2.0 provided by `catalogue`.
* ensure vectors data is stored on right device
* ensure the added vector is on the right device
* move vector to numpy before iterating
* move best_rows to numpy before iterating
* Terminology: deprecated vs obsolete
Typically, deprecated is used for functionality that is bound to become unavailable but that can still be used. Obsolete is used for features that have been removed. In E941, I think what is meant is "obsolete" since loading a model by a shortcut simply does not work anymore (and throws an error). This is different from downloading a model with a shortcut, which is deprecated but still works.
In light of this, perhaps all other error codes should be checked as well.
* clarify that the link command is removed and not just deprecated
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
* Update debug data further for v3
* Remove new/existing label distinction (new labels are not immediately
distinguishable because the pipeline is already initialized)
* Warn on missing labels in training data for all components except parser
* Separate textcat and textcat_multilabel sections
* Add section for morphologizer
* Reword missing label warnings
* Make vocab update in get_docs deterministic
The attribute `DocBin.strings` is a set. In `DocBin.get_docs`
a given vocab is updated by iterating over this set.
Iteration over a python set produces an arbitrary ordering,
therefore vocab is updated non-deterministically.
When training (fine-tuning) a spacy model, the base model's
vocabulary will be updated with the new vocabulary in the
training data in exactly the way described above. After
serialization, the file `model/vocab/strings.json` will
be sorted in an arbitrary way. This prevents reproducible
model training.
* Revert "Make vocab update in get_docs deterministic"
This reverts commit d6b87a2f55.
* Sort strings in StringStore serialization
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* extend span scorer with consider_label and allow_overlap
* unit test for spans y2x overlap
* add score_spans unit test
* docs for new fields in scorer.score_spans
* rename to include_label
* spell out if-else for clarity
* rename to 'labeled'
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Data in the JSON format is split into sentences, and each sentence is
saved with is_sent_start flags. Currently the flags are 1 for the first
token and 0 for the others. When deserialized this results in a pattern
of True, None, None, None... which makes single-sentence documents look
as though they haven't had sentence boundaries set.
Since items saved in JSON format have been split into sentences already,
the is_sent_start values should all be True or False.
* Support match alignments
* change naming from match_alignments to with_alignments, add conditional flow if with_alignments is given, validate with_alignments, add related test case
* remove added errors, utilize bint type, cleanup whitespace
* fix no new line in end of file
* Minor formatting
* Skip alignments processing if as_spans is set
* Add with_alignments to Matcher API docs
* Update website/docs/api/matcher.md
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Support infinite generators for training corpora
Support a training corpus with an infinite generator in the `spacy
train` training loop:
* Revert `create_train_batches` to the state where an infinite generator
can be used as the in the first epoch of exactly one epoch without
resulting in a memory leak (`max_epochs != 1` will still result in a
memory leak)
* Move the shuffling for the first epoch into the corpus reader,
renaming it to `spacy.Corpus.v2`.
* Switch to training option for shuffling in memory
Training loop:
* Add option `training.shuffle_train_corpus_in_memory` that controls
whether the corpus is loaded in memory once and shuffled in the training
loop
* Revert changes to `create_train_batches` and rename to
`create_train_batches_with_shuffling` for use with `spacy.Corpus.v1` and
a corpus that should be loaded in memory
* Add `create_train_batches_without_shuffling` for a corpus that
should not be shuffled in the training loop: the corpus is merely
batched during training
Corpus readers:
* Restore `spacy.Corpus.v1`
* Add `spacy.ShuffledCorpus.v1` for a corpus shuffled in memory in the
reader instead of the training loop
* In combination with `shuffle_train_corpus_in_memory = False`, each
epoch could result in a different augmentation
* Refactor create_train_batches, validation
* Rename config setting to `training.shuffle_train_corpus`
* Refactor to use a single `create_train_batches` method with a
`shuffle` option
* Only validate `get_examples` in initialize step if:
* labels are required
* labels are not provided
* Switch back to max_epochs=-1 for streaming train corpus
* Use first 100 examples for stream train corpus init
* Always check validate_get_examples in initialize
* Add failing test for PRFScore
* Fix erroneous implementation of __add__
* Simplify constructor
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
