Commit Graph

861 Commits

Author SHA1 Message Date
adrianeboyd 392c4880d9 Restructure Example with merged sents as default (#4632)
* Switch to train_dataset() function in train CLI

* Fixes for pipe() methods in pipeline components

* Don't clobber `examples` variable with `as_example` in pipe() methods
* Remove unnecessary traversals of `examples`

* Update Parser.pipe() for Examples

* Add `as_examples` kwarg to `pipe()` with implementation to return
`Example`s

* Accept `Doc` or `Example` in `pipe()` with `_get_doc()` (copied from
`Pipe`)

* Fixes to Example implementation in spacy.gold

* Move `make_projective` from an attribute of Example to an argument of
`Example.get_gold_parses()`

* Head of 0 are not treated as unset

* Unset heads are set to self rather than `None` (which causes problems
while projectivizing)

* Check for `Doc` (not just not `None`) when creating GoldParses for
pre-merged example

* Don't clobber `examples` variable in `iter_gold_docs()`

* Add/modify gold tests for handling projectivity

* In JSON roundtrip compare results from `dev_dataset` rather than
`train_dataset` to avoid projectivization (and other potential
modifications)

* Add test for projective train vs. nonprojective dev versions of the
same `Doc`

* Handle ignore_misaligned as arg rather than attr

Move `ignore_misaligned` from an attribute of `Example` to an argument
to `Example.get_gold_parses()`, which makes it parallel to
`make_projective`.

Add test with old and new align that checks whether `ignore_misaligned`
errors are raised as expected (only for new align).

* Remove unused attrs from gold.pxd

Remove `ignore_misaligned` and `make_projective` from `gold.pxd`

* Restructure Example with merged sents as default

An `Example` now includes a single `TokenAnnotation` that includes all
the information from one `Doc` (=JSON `paragraph`). If required, the
individual sentences can be returned as a list of examples with
`Example.split_sents()` with no raw text available.

* Input/output a single `Example.token_annotation`

* Add `sent_starts` to `TokenAnnotation` to handle sentence boundaries

* Replace `Example.merge_sents()` with `Example.split_sents()`

* Modify components to use a single `Example.token_annotation`

  * Pipeline components
  * conllu2json converter

* Rework/rename `add_token_annotation()` and `add_doc_annotation()` to
`set_token_annotation()` and `set_doc_annotation()`, functions that set
rather then appending/extending.

* Rename `morphology` to `morphs` in `TokenAnnotation` and `GoldParse`

* Add getters to `TokenAnnotation` to supply default values when a given
attribute is not available

* `Example.get_gold_parses()` in `spacy.gold._make_golds()` is only
applied on single examples, so the `GoldParse` is returned saved in the
provided `Example` rather than creating a new `Example` with no other
internal annotation

* Update tests for API changes and `merge_sents()` vs. `split_sents()`

* Refer to Example.goldparse in iter_gold_docs()

Use `Example.goldparse` in `iter_gold_docs()` instead of `Example.gold`
because a `None` `GoldParse` is generated with ignore_misaligned and
generating it on-the-fly can raise an unwanted AlignmentError

* Fix make_orth_variants()

Fix bug in make_orth_variants() related to conversion from multiple to
one TokenAnnotation per Example.

* Add basic test for make_orth_variants()

* Replace try/except with conditionals

* Replace default morph value with set
2019-11-25 16:03:28 +01:00
adrianeboyd 44829950ba Fix Example details for train CLI / pipeline components (#4624)
* Switch to train_dataset() function in train CLI

* Fixes for pipe() methods in pipeline components

* Don't clobber `examples` variable with `as_example` in pipe() methods
* Remove unnecessary traversals of `examples`

* Update Parser.pipe() for Examples

* Add `as_examples` kwarg to `pipe()` with implementation to return
`Example`s

* Accept `Doc` or `Example` in `pipe()` with `_get_doc()` (copied from
`Pipe`)

* Fixes to Example implementation in spacy.gold

* Move `make_projective` from an attribute of Example to an argument of
`Example.get_gold_parses()`

* Head of 0 are not treated as unset

* Unset heads are set to self rather than `None` (which causes problems
while projectivizing)

* Check for `Doc` (not just not `None`) when creating GoldParses for
pre-merged example

* Don't clobber `examples` variable in `iter_gold_docs()`

* Add/modify gold tests for handling projectivity

* In JSON roundtrip compare results from `dev_dataset` rather than
`train_dataset` to avoid projectivization (and other potential
modifications)

* Add test for projective train vs. nonprojective dev versions of the
same `Doc`

* Handle ignore_misaligned as arg rather than attr

Move `ignore_misaligned` from an attribute of `Example` to an argument
to `Example.get_gold_parses()`, which makes it parallel to
`make_projective`.

Add test with old and new align that checks whether `ignore_misaligned`
errors are raised as expected (only for new align).

* Remove unused attrs from gold.pxd

Remove `ignore_misaligned` and `make_projective` from `gold.pxd`

* Refer to Example.goldparse in iter_gold_docs()

Use `Example.goldparse` in `iter_gold_docs()` instead of `Example.gold`
because a `None` `GoldParse` is generated with ignore_misaligned and
generating it on-the-fly can raise an unwanted AlignmentError

* Update test for ignore_misaligned
2019-11-23 14:32:15 +01:00
Sofie Van Landeghem e48a09df4e Example class for training data (#4543)
* OrigAnnot class instead of gold.orig_annot list of zipped tuples

* from_orig to replace from_annot_tuples

* rename to RawAnnot

* some unit tests for GoldParse creation and internal format

* removing orig_annot and switching to lists instead of tuple

* rewriting tuples to use RawAnnot (+ debug statements, WIP)

* fix pop() changing the data

* small fixes

* pop-append fixes

* return RawAnnot for existing GoldParse to have uniform interface

* clean up imports

* fix merge_sents

* add unit test for 4402 with new structure (not working yet)

* introduce DocAnnot

* typo fixes

* add unit test for merge_sents

* rename from_orig to from_raw

* fixing unit tests

* fix nn parser

* read_annots to produce text, doc_annot pairs

* _make_golds fix

* rename golds_to_gold_annots

* small fixes

* fix encoding

* have golds_to_gold_annots use DocAnnot

* missed a spot

* merge_sents as function in DocAnnot

* allow specifying only part of the token-level annotations

* refactor with Example class + underlying dicts

* pipeline components to work with Example objects (wip)

* input checking

* fix yielding

* fix calls to update

* small fixes

* fix scorer unit test with new format

* fix kwargs order

* fixes for ud and conllu scripts

* fix reading data for conllu script

* add in proper errors (not fixed numbering yet to avoid merge conflicts)

* fixing few more small bugs

* fix EL script
2019-11-11 17:35:27 +01:00
Matthew Honnibal d5509e0989 Support Mish activation (requires Thinc 7.3) (#4536)
* Add arch for MishWindowEncoder

* Support mish in tok2vec and conv window >=2

* Pass new tok2vec settings from parser

* Syntax error

* Fix tok2vec setting

* Fix registration of MishWindowEncoder

* Fix receptive field setting

* Fix mish arch

* Pass more options from parser

* Support more tok2vec options in pretrain

* Require thinc 7.3

* Add docs [ci skip]

* Require thinc 7.3.0.dev0 to run CI

* Run black

* Fix typo

* Update Thinc version


Co-authored-by: Ines Montani <ines@ines.io>
2019-10-28 15:16:33 +01:00
Matthew Honnibal 795699015c
Clarify parser model CPU/GPU code (#4535)
The previous version worked with previous thinc, but only
because some thinc ops happened to have gpu/cpu compatible
implementations. It's better to call the right Ops instance.
2019-10-27 23:43:09 +01:00
Matthew Honnibal f8d740bfb1
Fix --gold-preproc train cli command (#4392)
* Fix get labels for textcat

* Fix char_embed for gpu

* Revert "Fix char_embed for gpu"

This reverts commit 055b9a9e85.

* Fix passing of cats in gold.pyx

* Revert "Match pop with append for training format (#4516)"

This reverts commit 8e7414dace.

* Fix popping gold parses

* Fix handling of cats in gold tuples

* Fix name

* Fix ner_multitask_objective script

* Add test for 4402
2019-10-27 21:58:50 +01:00
Sofie Van Landeghem 8e7414dace Match pop with append for training format (#4516)
* trying to fix script - not succesful yet

* match pop() with extend() to avoid changing the data

* few more pop-extend fixes

* reinsert deleted print statement

* fix print statement

* add last tested version

* append instead of extend

* add in few comments

* quick fix for 4402 + unit test

* fixing number of docs (not counting cats)

* more fixes

* fix len

* print tmp file instead of using data from examples dir

* print tmp file instead of using data from examples dir (2)
2019-10-27 16:01:32 +01:00
Ines Montani a9c6104047 Component decorator and component analysis (#4517)
* Add work in progress

* Update analysis helpers and component decorator

* Fix porting of docstrings for Python 2

* Fix docstring stuff on Python 2

* Support meta factories when loading model

* Put auto pipeline analysis behind flag for now

* Analyse pipes on remove_pipe and replace_pipe

* Move analysis to root for now

Try to find a better place for it, but it needs to go for now to avoid circular imports

* Simplify decorator

Don't return a wrapped class and instead just write to the object

* Update existing components and factories

* Add condition in factory for classes vs. functions

* Add missing from_nlp classmethods

* Add "retokenizes" to printed overview

* Update assigns/requires declarations of builtins

* Only return data if no_print is enabled

* Use multiline table for overview

* Don't support Span

* Rewrite errors/warnings and move them to spacy.errors
2019-10-27 13:35:49 +01:00
Sofie Van Landeghem 99e309bb19 fix nn parser sample construction (#4524) 2019-10-25 22:26:42 +02:00
Sofie Van Landeghem 48886afc78 prevent zero-length mem alloc (#4429)
* raise specific error when removing a matcher rule that doesn't exist

* rephrasing

* goldparse init: allocate fields only if doc is not empty

* avoid zero length alloc in saving tokenizer cache

* avoid allocating zero length mem in matcher

* asserts to avoid allocating zero length mem

* fix zero-length allocation in matcher

* bump cymem version

* revert cymem version bump
2019-10-22 16:54:33 +02:00
adrianeboyd 3dfc764577 Free pointers in parser activations (#4486)
* Free pointers in ActivationsC

* Restructure alloc/free for parser activations

* Rewrite/restructure to have allocation and free in parallel functions
in `_parser_model` rather than partially in `_parseC()` in `Parser`.

* Remove `resize_activations` from `_parser_model.pxd`.
2019-10-22 15:06:44 +02:00
adrianeboyd d359da9687 Replace Entity/MatchStruct with SpanC (#4459)
* Replace MatchStruct with Entity

Replace MatchStruct with Entity since the existing Entity struct is
nearly identical.

* Replace Entity with more general SpanC
2019-10-18 11:01:47 +02:00
Sofie Van Landeghem 22b9e12159 Ensure the NER remains consistent after resizing (#4330)
* test and fix for second bug of issue 4042

* fix for first bug in 4042

* crashing test for Issue 4313

* forgot one instance of resize

* remove prints

* undo uncomment

* delete test for 4313 (uses third party lib)

* add fix for Issue 4313

* unit test for 4313
2019-09-27 20:57:13 +02:00
Ines Montani 16aa092fb5 Improve Morphology errors (#4314)
* Improve Morphology errors

* Also clean up some other errors

* Update errors.py
2019-09-21 14:37:06 +02:00
Matthew Honnibal 46c02d25b1 Merge changes to test_ner 2019-09-18 21:41:24 +02:00
Sofie Van Landeghem de5a9ecdf3 Distinction between outside, missing and blocked NER annotations (#4307)
* remove duplicate unit test

* unit test (currently failing) for issue 4267

* bugfix: ensure doc.ents preserves kb_id annotations

* fix in setting doc.ents with empty label

* rename

* test for presetting an entity to a certain type

* allow overwriting Outside + blocking presets

* fix actions when previous label needs to be kept

* fix default ent_iob in set entities

* cleaner solution with U- action

* remove debugging print statements

* unit tests with explicit transitions and is_valid testing

* remove U- from move_names explicitly

* remove unit tests with pre-trained models that don't work

* remove (working) unit tests with pre-trained models

* clean up unit tests

* move unit tests

* small fixes

* remove two TODO's from doc.ents comments
2019-09-18 21:37:17 +02:00
Ines Montani 691e0088cf Remove duplicate tok2vec property (closes #4302) 2019-09-17 11:22:03 +02:00
adrianeboyd b5d999e510 Add textcat to train CLI (#4226)
* Add doc.cats to spacy.gold at the paragraph level

Support `doc.cats` as `"cats": [{"label": string, "value": number}]` in
the spacy JSON training format at the paragraph level.

* `spacy.gold.docs_to_json()` writes `docs.cats`

* `GoldCorpus` reads in cats in each `GoldParse`

* Update instances of gold_tuples to handle cats

Update iteration over gold_tuples / gold_parses to handle addition of
cats at the paragraph level.

* Add textcat to train CLI

* Add textcat options to train CLI
* Add textcat labels in `TextCategorizer.begin_training()`
* Add textcat evaluation to `Scorer`:
  * For binary exclusive classes with provided label: F1 for label
  * For 2+ exclusive classes: F1 macro average
  * For multilabel (not exclusive): ROC AUC macro average (currently
relying on sklearn)
* Provide user info on textcat evaluation settings, potential
incompatibilities
* Provide pipeline to Scorer in `Language.evaluate` for textcat config
* Customize train CLI output to include only metrics relevant to current
pipeline
* Add textcat evaluation to evaluate CLI

* Fix handling of unset arguments and config params

Fix handling of unset arguments and model confiug parameters in Scorer
initialization.

* Temporarily add sklearn requirement

* Remove sklearn version number

* Improve Scorer handling of models without textcats

* Fixing Scorer handling of models without textcats

* Update Scorer output for python 2.7

* Modify inf in Scorer for python 2.7

* Auto-format

Also make small adjustments to make auto-formatting with black easier and produce nicer results

* Move error message to Errors

* Update documentation

* Add cats to annotation JSON format [ci skip]

* Fix tpl flag and docs [ci skip]

* Switch to internal roc_auc_score

Switch to internal `roc_auc_score()` adapted from scikit-learn.

* Add AUCROCScore tests and improve errors/warnings

* Add tests for AUCROCScore and roc_auc_score
* Add missing error for only positive/negative values
* Remove unnecessary warnings and errors

* Make reduced roc_auc_score functions private

Because most of the checks and warnings have been stripped for the
internal functions and access is only intended through `ROCAUCScore`,
make the functions for roc_auc_score adapted from scikit-learn private.

* Check that data corresponds with multilabel flag

Check that the training instances correspond with the multilabel flag,
adding the multilabel flag if required.

* Add textcat score to early stopping check

* Add more checks to debug-data for textcat

* Add example training data for textcat

* Add more checks to textcat train CLI

* Check configuration when extending base model
* Fix typos

* Update textcat example data

* Provide licensing details and licenses for data
* Remove two labels with no positive instances from jigsaw-toxic-comment
data.


Co-authored-by: Ines Montani <ines@ines.io>
2019-09-15 22:31:31 +02:00
Adriane Boyd 6b0fec76fd Fix handling of preset entities in NER
* Fix check of valid ent_type for B
* Add valid L as preset-I followed by not-I
2019-09-04 13:42:42 +02:00
Matthew Honnibal bb911e5f4e Fix #3830: 'subtok' label being added even if learn_tokens=False (#4188)
* Prevent subtok label if not learning tokens

The parser introduces the subtok label to mark tokens that should be
merged during post-processing. Previously this happened even if we did
not have the --learn-tokens flag set. This patch passes the config
through to the parser, to prevent the problem.

* Make merge_subtokens a parser post-process if learn_subtokens

* Fix train script

* Add test for 3830: subtok problem

* Fix handlign of non-subtok in parser training
2019-08-23 17:54:00 +02:00
Matthew Honnibal 73e095923f 💫 Improve error message when model.from_bytes() dies (#4014)
* Improve error message when model.from_bytes() dies

When Thinc's model.from_bytes() is called with a mismatched model, often
we get a particularly ungraceful error,

e.g. "AttributeError: FunctionLayer has no attribute G"

This is because we're trying to load the parameters for something like
a LayerNorm layer, and the model architecture has some other layer there
instead. This is obviously terrible, especially since the error *type*
is wrong.

I've changed it to raise a ValueError. The error message is still
probably a bit terse, but it's hard to be sure exactly what's gone
wrong.

* Update spacy/pipeline/pipes.pyx

* Update spacy/pipeline/pipes.pyx

* Update spacy/pipeline/pipes.pyx

* Update spacy/syntax/nn_parser.pyx

* Update spacy/syntax/nn_parser.pyx

* Update spacy/pipeline/pipes.pyx

Co-Authored-By: Matthew Honnibal <honnibal+gh@gmail.com>

* Update spacy/pipeline/pipes.pyx

Co-Authored-By: Matthew Honnibal <honnibal+gh@gmail.com>


Co-authored-by: Ines Montani <ines@ines.io>
2019-07-24 11:27:34 +02:00
Matthew Honnibal d9a07a7f6e
💫 Fix class mismap on parser deserializing (closes #3433) (#3470)
v2.1 introduced a regression when deserializing the parser after
parser.add_label() had been called. The code around the class mapping is
pretty confusing currently, as it was written to accommodate backwards
model compatibility. It needs to be revised when the models are next
retrained.

Closes #3433
2019-03-23 13:46:25 +01:00
Matthew Honnibal 0a4b074184 Improve beam search defaults 2019-03-17 21:47:45 +01:00
Matthew Honnibal 58d562d9b0
Merge pull request #3416 from explosion/feature/improve-beam
Improve beam search support
2019-03-16 18:42:18 +01:00
Ines Montani cb5dbfa63a Tidy up references to n_threads and fix default 2019-03-15 16:24:26 +01:00
Matthew Honnibal b13b2aeb54 Use hash_state in beam 2019-03-15 15:22:58 +01:00
Matthew Honnibal 693c8934e8 Normalize over all actions in parser, not just valid ones 2019-03-15 15:22:16 +01:00
Matthew Honnibal b94b2b1168 Export hash_state from beam_utils 2019-03-15 15:20:28 +01:00
Matthew Honnibal 0703f5986b Remove hack from beam 2019-03-15 00:48:39 +01:00
Ines Montani 47e9c274ef Tidy up property code style (#3391)
Use decorator if properties only have a getter and existing syntax if there's getter and setter
2019-03-11 15:59:09 +01:00
Ines Montani 7ba3a5d95c 💫 Make serialization methods consistent (#3385)
* Make serialization methods consistent

exclude keyword argument instead of random named keyword arguments and deprecation handling

* Update docs and add section on serialization fields
2019-03-10 19:16:45 +01:00
Matthew Honnibal 7461e5e055 Fix batch bug in issue #3344 2019-03-10 16:01:34 +01:00
Matthew Honnibal a5b1f6dcec Fix NER when preset entities cross sentence boundaries (#3379)
💫 Fix NER when preset entities cross sentence boundaries
2019-03-10 14:53:03 +01:00
Matthew Honnibal bdc77848f5 Add helper method to apply a transition in parser/NER 2019-03-10 13:00:00 +01:00
Matthew Honnibal f1d77eb140
💫 Improve handling of missing NER tags (closes #2603) (#3341)
* Improve handling of missing NER tags

GoldParse can accept missing NER tags, if entities is provided
in BILUO format (rather than as spans). Missing tags can be provided
as None values.

Fix bug that occurred when first tag was a None value. Closes #2603.

* Document specification of missing NER tags.
2019-02-27 12:06:32 +01:00
Matthew Honnibal 1f7c56cd93 Fix parser.add_label() 2019-02-24 16:53:22 +01:00
Matthew Honnibal 0367f864fe Fix handling of added labels. Resolves #3189 2019-02-24 16:41:41 +01:00
Matthew Honnibal d74dbde828 Fix order of actions when labels added to parser
When labels were added to the parser or NER, we weren't loading back the
classes in the correct order. Re issue #3189
2019-02-24 16:36:29 +01:00
Matthew Honnibal d396a69c7b More fixes for issue #3112 2019-02-21 17:12:23 +01:00
Matthew Honnibal f31dbec528 More fixes for #3112 2019-02-21 11:10:10 +01:00
Matthew Honnibal 6574e4f2d3 Fix issue #3112 part 1 2019-02-21 09:27:38 +01:00
Matthew Honnibal f57bea8ab6
💫 Prevent parser from predicting unseen classes (#3075)
The output weights often return negative scores for classes, especially
via the bias terms. This means that when we add a new class, we can't
rely on just zeroing the weights, or we'll end up with positive
predictions for those labels.

To solve this, we use nan values as the initial weights for new labels.
This prevents them from ever coming out on top. During backprop, we
replace the nan values with the minimum assigned score, so that we're
still able to learn these classes.
2018-12-20 16:12:22 +01:00
Matthew Honnibal 9ec9f89b99 💫 Raise better error when using uninitialized pipeline component (#3074)
After creating a component, the `.model` attribute is left with the value `True`, to indicate it should be created later during `from_disk()`, `from_bytes()` or `begin_training()`. This had led to confusing errors if you try to use the component without initializing the model.

To fix this, we add a method `require_model()` to the `Pipe` base class. The `require_model()` method needs to be called at the start of the `.predict()` and `.update()` methods of the components. It raises a `ValueError` if the model is not initialized. An error message has been added to `spacy.errors`.
2018-12-20 15:54:53 +01:00
Matthew Honnibal e24f94ce39 Fix handling of preset entities. closes #2779 2018-12-19 02:13:31 +01:00
Matthew Honnibal faa8656582 Port parser fix for large label sets from master 2018-12-19 02:11:26 +01:00
Matthew Honnibal 99a84e4d0e Make ParserModel.resize_output idempotent 2018-12-19 02:10:36 +01:00
Ines Montani 61d09c481b Merge branch 'master' into develop 2018-12-18 13:48:10 +01:00
Matthew Honnibal 83ac227bd3
💫 Better support for semi-supervised learning (#3035)
The new spacy pretrain command implemented BERT/ULMFit/etc-like transfer learning, using our Language Modelling with Approximate Outputs version of BERT's cloze task. Pretraining is convenient, but in some ways it's a bit of a strange solution. All we're doing is initialising the weights. At the same time, we're putting a lot of work into our optimisation so that it's less sensitive to initial conditions, and more likely to find good optima. I discuss this a bit in the pseudo-rehearsal blog post: https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting
Support semi-supervised learning in spacy train

One obvious way to improve these pretraining methods is to do multi-task learning, instead of just transfer learning. This has been shown to work very well: https://arxiv.org/pdf/1809.08370.pdf . This patch makes it easy to do this sort of thing.

    Add a new argument to spacy train, --raw-text. This takes a jsonl file with unlabelled data that can be used in arbitrary ways to do semi-supervised learning.

    Add a new method to the Language class and to pipeline components, .rehearse(). This is like .update(), but doesn't expect GoldParse objects. It takes a batch of Doc objects, and performs an update on some semi-supervised objective.

    Move the BERT-LMAO objective out from spacy/cli/pretrain.py into spacy/_ml.py, so we can create a new pipeline component, ClozeMultitask. This can be specified as a parser or NER multitask in the spacy train command. Example usage:

python -m spacy train en ./tmp ~/data/en-core-web/train/nw.json ~/data/en-core-web/dev/nw.json --pipeline parser --raw-textt ~/data/unlabelled/reddit-100k.jsonl --vectors en_vectors_web_lg --parser-multitasks cloze

Implement rehearsal methods for pipeline components

The new --raw-text argument and nlp.rehearse() method also gives us a good place to implement the the idea in the pseudo-rehearsal blog post in the parser. This works as follows:

    Add a new nlp.resume_training() method. This allocates copies of pre-trained models in the pipeline, setting things up for the rehearsal updates. It also returns an optimizer object. This also greatly reduces confusion around the nlp.begin_training() method, which randomises the weights, making it not suitable for adding new labels or otherwise fine-tuning a pre-trained model.

    Implement rehearsal updates on the Parser class, making it available for the dependency parser and NER. During rehearsal, the initial model is used to supervise the model being trained. The current model is asked to match the predictions of the initial model on some data. This minimises catastrophic forgetting, by keeping the model's predictions close to the original. See the blog post for details.

    Implement rehearsal updates for tagger

    Implement rehearsal updates for text categoriz
2018-12-10 16:25:33 +01:00
Matthew Honnibal 6936ca1664 Merge branch 'develop' of https://github.com/explosion/spaCy into develop 2018-12-10 09:44:07 +01:00
Matthew Honnibal 4405b5c875 Fix resizing edge-case for NER 2018-12-10 06:25:17 +00:00