Commit Graph

153 Commits

Author SHA1 Message Date
Sofie Van Landeghem
780d43aac7 fix bug in EL predict (#4779) 2019-12-06 19:18:14 +01:00
adrianeboyd
b841d3fe75 Add a tagger-based SentenceRecognizer (#4713)
* Add sent_starts to GoldParse

* Add SentTagger pipeline component

Add `SentTagger` pipeline component as a subclass of `Tagger`.

* Model reduces default parameters from `Tagger` to be small and fast
* Hard-coded set of two labels:
  * S (1): token at beginning of sentence
  * I (0): all other sentence positions
* Sets `token.sent_start` values

* Add sentence segmentation to Scorer

Report `sent_p/r/f` for sentence boundaries, which may be provided by
various pipeline components.

* Add sentence segmentation to CLI evaluate

* Add senttagger metrics/scoring to train CLI

* Rename SentTagger to SentenceRecognizer

* Add SentenceRecognizer to spacy.pipes imports

* Add SentenceRecognizer serialization test

* Shorten component name to sentrec

* Remove duplicates from train CLI output metrics
2019-11-28 11:10:07 +01:00
adrianeboyd
48ea2e8d0f Restructure Sentencizer to follow Pipe API (#4721)
* Restructure Sentencizer to follow Pipe API

Restructure Sentencizer to follow Pipe API so that it can be scored with
`nlp.evaluate()`.

* Add Sentencizer pipe() test
2019-11-27 16:33:34 +01:00
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
adrianeboyd
054df5d90a Add error for non-string labels (#4690)
Add error when attempting to add non-string labels to `Tagger` or
`TextCategorizer`.
2019-11-21 16:24:10 +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
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
Zhuoru Lin
10d88b09bb Bugfix/fix wikidata train entity linker (#4509)
* Fix labels_discard Nonetype iteration error

* Contributor agreement for Zhuoru Lin

* Enhance EntityLinker.predict() to handle labels_discard is None case.
2019-10-24 12:52:59 +02:00
Sofie Van Landeghem
2d249a9502 KB extensions and better parsing of WikiData (#4375)
* fix overflow error on windows

* more documentation & logging fixes

* md fix

* 3 different limit parameters to play with execution time

* bug fixes directory locations

* small fixes

* exclude dev test articles from prior probabilities stats

* small fixes

* filtering wikidata entities, removing numeric and meta items

* adding aliases from wikidata also to the KB

* fix adding WD aliases

* adding also new aliases to previously added entities

* fixing comma's

* small doc fixes

* adding subclassof filtering

* append alias functionality in KB

* prevent appending the same entity-alias pair

* fix for appending WD aliases

* remove date filter

* remove unnecessary import

* small corrections and reformatting

* remove WD aliases for now (too slow)

* removing numeric entities from training and evaluation

* small fixes

* shortcut during prediction if there is only one candidate

* add counts and fscore logging, remove FP NER from evaluation

* fix entity_linker.predict to take docs instead of single sentences

* remove enumeration sentences from the WP dataset

* entity_linker.update to process full doc instead of single sentence

* spelling corrections and dump locations in readme

* NLP IO fix

* reading KB is unnecessary at the end of the pipeline

* small logging fix

* remove empty files
2019-10-14 12:28:53 +02:00
Matthew Honnibal
29f9fec267
Improve spacy pretrain (#4393)
* Support bilstm_depth arg in spacy pretrain

* Add option to ignore zero vectors in get_cossim_loss

* Use cosine loss in Cloze multitask
2019-10-07 23:34:58 +02:00
Sofie Van Landeghem
9d3ce7cba2 Ensure training doesn't crash with empty batches (#4360)
* unit test for previously resolved unflatten issue

* prevent batch of empty docs to cause problems
2019-10-02 12:50:47 +02:00
Ines Montani
b6670bf0c2 Use consistent spelling 2019-10-02 10:37:39 +02:00
Ines Montani
3297a19545 Warn in Tagger.begin_training if no lemma tables are available (#4351) 2019-10-01 15:13:55 +02:00
Matthew Honnibal
46c02d25b1 Merge changes to test_ner 2019-09-18 21:41:24 +02:00
tamuhey
875f3e5d8c remove redundant __call__ method in pipes.TextCategorizer (#4305)
* remove redundant __call__ method in pipes.TextCategorizer

Because the parent __call__ method behaves in the same way.

* fix: Pipe.__call__ arg

* fix: invalid arg in Pipe.__call__

* modified:   spacy/tests/regression/test_issue4278.py (#4278)

* deleted:    Pipfile
2019-09-18 21:31:27 +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
Ines Montani
16c2522791 Merge branch 'master' into develop 2019-09-14 16:42:01 +02:00
adrianeboyd
6942a6a69b Extend default punct for sentencizer (#4290)
Most of these characters are for languages / writing systems that aren't
supported by spacy, but I don't think it causes problems to include
them. In the UD evals, Hindi and Urdu improve a lot as expected (from
0-10% to 70-80%) and Persian improves a little (90% to 96%). Tamil
improves in combination with #4288.

The punctuation list is converted to a set internally because of its
increased length.

Sentence final punctuation generated with:

```
unichars -gas '[\p{Sentence_Break=STerm}\p{Sentence_Break=ATerm}]' '\p{Terminal_Punctuation}'
```

See: https://stackoverflow.com/a/9508766/461847

Fixes #4269.
2019-09-14 15:25:48 +02:00
Ines Montani
27106d6528 Merge branch 'master' into develop 2019-09-13 17:07:17 +02:00
Sofie Van Landeghem
2ae5db580e dim bugfix when incl_prior is False (#4285) 2019-09-13 16:30:05 +02:00
Ines Montani
3c3658ef9f Merge branch 'master' into develop 2019-09-12 18:03:01 +02:00
Ines Montani
228bbf506d Improve label properties on pipes 2019-09-12 18:02:44 +02:00
Ines Montani
655b434553 Merge branch 'master' into develop 2019-09-12 11:39:18 +02:00
tamuhey
71909cdf22 Fix iss4278 (#4279)
* fix: len(tuple) == 2

* (#4278) add fail test

* add contributor's aggreement
2019-09-12 10:44:49 +02:00
Ines Montani
8ebc3711dc Fix bug in Parser.labels and add test (#4275) 2019-09-11 18:29:35 +02:00
Matthew Honnibal
c308cf3e3e
Merge branch 'master' into feature/lemmatizer 2019-08-25 13:52:27 +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
bcd08f20af Merge changes from master 2019-08-21 14:18:52 +02:00
Ines Montani
f65e36925d Fix absolute imports and avoid importing from cli 2019-08-20 15:08:59 +02:00
Sofie Van Landeghem
0ba1b5eebc CLI scripts for entity linking (wikipedia & generic) (#4091)
* document token ent_kb_id

* document span kb_id

* update pipeline documentation

* prior and context weights as bool's instead

* entitylinker api documentation

* drop for both models

* finish entitylinker documentation

* small fixes

* documentation for KB

* candidate documentation

* links to api pages in code

* small fix

* frequency examples as counts for consistency

* consistent documentation about tensors returned by predict

* add entity linking to usage 101

* add entity linking infobox and KB section to 101

* entity-linking in linguistic features

* small typo corrections

* training example and docs for entity_linker

* predefined nlp and kb

* revert back to similarity encodings for simplicity (for now)

* set prior probabilities to 0 when excluded

* code clean up

* bugfix: deleting kb ID from tokens when entities were removed

* refactor train el example to use either model or vocab

* pretrain_kb example for example kb generation

* add to training docs for KB + EL example scripts

* small fixes

* error numbering

* ensure the language of vocab and nlp stay consistent across serialization

* equality with =

* avoid conflict in errors file

* add error 151

* final adjustements to the train scripts - consistency

* update of goldparse documentation

* small corrections

* push commit

* turn kb_creator into CLI script (wip)

* proper parameters for training entity vectors

* wikidata pipeline split up into two executable scripts

* remove context_width

* move wikidata scripts in bin directory, remove old dummy script

* refine KB script with logs and preprocessing options

* small edits

* small improvements to logging of EL CLI script
2019-08-13 15:38:59 +02:00
Matthew Honnibal
4632c597e7 Fix Pipe base class 2019-08-01 17:29:01 +02:00
Sofie Van Landeghem
7de3b129ab Resolve edge case when calling textcat.predict with empty doc (#4035)
* resolve edge case where no doc has tokens when calling textcat.predict

* more explicit value test
2019-07-30 14:58:01 +02:00
Matthew Honnibal
06eb428ed1 Make pipe base class a bit less presumptuous 2019-07-28 17:56:11 +02:00
Matthew Honnibal
16b5144095 Don't raise NotImplemented in Pipe.update 2019-07-28 17:54:11 +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
svlandeg
4e7ec1ed31 return fix 2019-07-23 14:23:58 +02:00
svlandeg
400ff342cf replace assert's with custom error messages 2019-07-23 11:52:48 +02:00
svlandeg
20389e4553 format and bugfix 2019-07-22 15:08:17 +02:00
svlandeg
41fb5204ba output tensors as part of predict 2019-07-19 14:47:36 +02:00
svlandeg
21176517a7 have gold.links correspond exactly to doc.ents 2019-07-19 12:36:15 +02:00
svlandeg
e1213eaf6a use original gold object in get_loss function 2019-07-18 13:35:10 +02:00
svlandeg
ec55d2fccd filter training data beforehand (+black formatting) 2019-07-18 10:22:24 +02:00
svlandeg
a63d15a142 code cleanup 2019-07-15 17:36:43 +02:00
svlandeg
60f299374f set default context width 2019-07-15 12:03:09 +02:00
Sofie Van Landeghem
c4c21cb428 more friendly textcat errors (#3946)
* more friendly textcat errors with require_model and require_labels

* update thinc version with recent bugfix
2019-07-10 19:39:38 +02:00
Ines Montani
f2ea3e3ea2
Merge branch 'master' into feature/nel-wiki 2019-07-09 21:57:47 +02:00
Ines Montani
547464609d Remove merge_subtokens from parser postprocessing for now 2019-07-09 21:50:30 +02:00
svlandeg
668b17ea4a deuglify kb deserializer 2019-07-03 15:00:42 +02:00
svlandeg
8840d4b1b3 fix for context encoder optimizer 2019-07-03 13:35:36 +02:00
svlandeg
2d2dea9924 experiment with adding NER types to the feature vector 2019-06-29 14:52:36 +02:00
svlandeg
c664f58246 adding prior probability as feature in the model 2019-06-28 16:22:58 +02:00
svlandeg
68a0662019 context encoder with Tok2Vec + linking model instead of cosine 2019-06-28 08:29:31 +02:00
Ines Montani
37f744ca00 Auto-format [ci skip] 2019-06-26 14:48:09 +02:00
svlandeg
1de61f68d6 improve speed of prediction loop 2019-06-26 13:53:10 +02:00
svlandeg
58a5b40ef6 clean up duplicate code 2019-06-24 15:19:58 +02:00
svlandeg
b58bace84b small fixes 2019-06-24 10:55:04 +02:00
svlandeg
cc9ae28a52 custom error and warning messages 2019-06-19 12:35:26 +02:00
svlandeg
791327e3c5 Merge remote-tracking branch 'upstream/master' into feature/nel-wiki 2019-06-19 09:44:05 +02:00
svlandeg
a31648d28b further code cleanup 2019-06-19 09:15:43 +02:00
svlandeg
478305cd3f small tweaks and documentation 2019-06-18 18:38:09 +02:00
svlandeg
0d177c1146 clean up code, remove old code, move to bin 2019-06-18 13:20:40 +02:00
svlandeg
ffae7d3555 sentence encoder only (removing article/mention encoder) 2019-06-18 00:05:47 +02:00
svlandeg
b312f2d0e7 redo training data to be independent of KB and entity-level instead of doc-level 2019-06-14 15:55:26 +02:00
svlandeg
78dd3e11da write entity linking pipe to file and keep vocab consistent between kb and nlp 2019-06-13 16:25:39 +02:00
svlandeg
b12001f368 small fixes 2019-06-12 22:05:53 +02:00
svlandeg
6521cfa132 speeding up training 2019-06-12 13:37:05 +02:00
svlandeg
fe1ed432ef eval on dev set, varying combo's of prior and context scores 2019-06-11 11:40:58 +02:00
svlandeg
83dc7b46fd first tests with EL pipe 2019-06-10 21:25:26 +02:00
Matthew Honnibal
a931d72459 Add merge_subtokens as parser post-process. Re #3830 2019-06-07 20:40:41 +02:00
svlandeg
7de1ee69b8 training loop in proper pipe format 2019-06-07 15:55:10 +02:00
svlandeg
0486ccabfd introduce goldparse.links 2019-06-07 13:54:45 +02:00
svlandeg
a5c061f506 storing NEL training data in GoldParse objects 2019-06-07 12:58:42 +02:00
svlandeg
61f0e2af65 code cleanup 2019-06-06 20:22:14 +02:00
svlandeg
5c723c32c3 entity vectors in the KB + serialization of them 2019-06-05 18:29:18 +02:00
svlandeg
9abbd0899f separate entity encoder to get 64D descriptions 2019-06-05 00:09:46 +02:00
svlandeg
fb37cdb2d3 implementing el pipe in pipes.pyx (not tested yet) 2019-06-03 21:32:54 +02:00
svlandeg
dd691d0053 debugging 2019-05-17 17:44:11 +02:00
Sofie
a4a6bfa4e1
Merge branch 'master' into feature/el-framework 2019-03-26 11:00:02 +01:00
svlandeg
8814b9010d entity as one field instead of both ID and name 2019-03-25 18:10:41 +01:00
Matthew Honnibal
6c783f8045 Bug fixes and options for TextCategorizer (#3472)
* Fix code for bag-of-words feature extraction

The _ml.py module had a redundant copy of a function to extract unigram
bag-of-words features, except one had a bug that set values to 0.
Another function allowed extraction of bigram features. Replace all three
with a new function that supports arbitrary ngram sizes and also allows
control of which attribute is used (e.g. ORTH, LOWER, etc).

* Support 'bow' architecture for TextCategorizer

This allows efficient ngram bag-of-words models, which are better when
the classifier needs to run quickly, especially when the texts are long.
Pass architecture="bow" to use it. The extra arguments ngram_size and
attr are also available, e.g. ngram_size=2 means unigram and bigram
features will be extracted.

* Fix size limits in train_textcat example

* Explain architectures better in docs
2019-03-23 16:44:44 +01:00
Ines Montani
06bf130890 💫 Add better and serializable sentencizer (#3471)
* Add better serializable sentencizer component

* Replace default factory

* Add tests

* Tidy up

* Pass test

* Update docs
2019-03-23 15:45:02 +01:00
svlandeg
5318ce88fa 'entity_linker' instead of 'el' 2019-03-22 13:55:10 +01:00
svlandeg
1ee0e78fd7 select candidate with highest prior probabiity 2019-03-22 11:36:45 +01:00
svlandeg
c593607ce2 minimal EL pipe 2019-03-22 11:36:45 +01:00
svlandeg
735fc2a735 annotate kb_id through ents in doc 2019-03-22 11:36:44 +01:00
svlandeg
d849eb2455 adding kb_id as field to token, el as nlp pipeline component 2019-03-22 11:34:46 +01:00
Ines Montani
278e9d2eb0 Merge branch 'master' into feature/lemmatizer 2019-03-16 13:44:22 +01:00
Ines Montani
cb5dbfa63a Tidy up references to n_threads and fix default 2019-03-15 16:24:26 +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
0f12082465 Refactor morphologizer 2019-03-09 22:54:59 +00:00
Matthew Honnibal
cc2b2dba14 Neaten set_morphology option on Tagger 2019-03-08 19:16:02 +01:00
Matthew Honnibal
afa227e25b Fix setter 2019-03-08 19:10:01 +01:00
Matthew Honnibal
b27bd42613 Fix compile error 2019-03-08 19:06:02 +01:00
Matthew Honnibal
c91577db02 Add set_morphology cfg option for Tagger 2019-03-08 19:03:17 +01:00
Ines Montani
296446a1c8
Tidy up and improve docs and docstrings (#3370)
<!--- Provide a general summary of your changes in the title. -->

## Description
* tidy up and adjust Cython code to code style
* improve docstrings and make calling `help()` nicer
* add URLs to new docs pages to docstrings wherever possible, mostly to user-facing objects
* fix various typos and inconsistencies in docs

### Types of change
enhancement, docs

## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
2019-03-08 11:42:26 +01:00
Matthew Honnibal
6b0008afc6 Clean up TextCategorizer slightly 2019-02-23 12:28:06 +01:00
Matthew Honnibal
ce1e4eace2 Default to former TextCategorizer model
* Keep TextCategorizer default model same as v2.0
* Add option 'architecture' that allows "simple_cnn" to switch to
simpler model.
* Add option exclusive_classes, defaulting to False. If set to True,
the model treats classes as mutually exclusive, i.e. only one class can
be true per instance.
2019-02-23 11:55:16 +01:00