* Add load_from_config function
* Add train_from_config script
* Merge configs and expose via spacy.config
* Fix script
* Suggest create_evaluation_callback
* Hard-code for NER
* Fix errors
* Register command
* Add TODO
* Update train-from-config todos
* Fix imports
* Allow delayed setting of parser model nr_class
* Get train-from-config working
* Tidy up and fix scores and printing
* Hide traceback if cancelled
* Fix weighted score formatting
* Fix score formatting
* Make output_path optional
* Add Tok2Vec component
* Tidy up and add tok2vec_tensors
* Add option to copy docs in nlp.update
* Copy docs in nlp.update
* Adjust nlp.update() for set_annotations
* Don't shuffle pipes in nlp.update, decruft
* Support set_annotations arg in component update
* Support set_annotations in parser update
* Add get_gradients method
* Add get_gradients to parser
* Update errors.py
* Fix problems caused by merge
* Add _link_components method in nlp
* Add concept of 'listeners' and ControlledModel
* Support optional attributes arg in ControlledModel
* Try having tok2vec component in pipeline
* Fix tok2vec component
* Fix config
* Fix tok2vec
* Update for Example
* Update for Example
* Update config
* Add eg2doc util
* Update and add schemas/types
* Update schemas
* Fix nlp.update
* Fix tagger
* Remove hacks from train-from-config
* Remove hard-coded config str
* Calculate loss in tok2vec component
* Tidy up and use function signatures instead of models
* Support union types for registry models
* Minor cleaning in Language.update
* Make ControlledModel specifically Tok2VecListener
* Fix train_from_config
* Fix tok2vec
* Tidy up
* Add function for bilstm tok2vec
* Fix type
* Fix syntax
* Fix pytorch optimizer
* Add example configs
* Update for thinc describe changes
* Update for Thinc changes
* Update for dropout/sgd changes
* Update for dropout/sgd changes
* Unhack gradient update
* Work on refactoring _ml
* Remove _ml.py module
* WIP upgrade cli scripts for thinc
* Move some _ml stuff to util
* Import link_vectors from util
* Update train_from_config
* Import from util
* Import from util
* Temporarily add ml.component_models module
* Move ml methods
* Move typedefs
* Update load vectors
* Update gitignore
* Move imports
* Add PrecomputableAffine
* Fix imports
* Fix imports
* Fix imports
* Fix missing imports
* Update CLI scripts
* Update spacy.language
* Add stubs for building the models
* Update model definition
* Update create_default_optimizer
* Fix import
* Fix comment
* Update imports in tests
* Update imports in spacy.cli
* Fix import
* fix obsolete thinc imports
* update srsly pin
* from thinc to ml_datasets for example data such as imdb
* update ml_datasets pin
* using STATE.vectors
* small fix
* fix Sentencizer.pipe
* black formatting
* rename Affine to Linear as in thinc
* set validate explicitely to True
* rename with_square_sequences to with_list2padded
* rename with_flatten to with_list2array
* chaining layernorm
* small fixes
* revert Optimizer import
* build_nel_encoder with new thinc style
* fixes using model's get and set methods
* Tok2Vec in component models, various fixes
* fix up legacy tok2vec code
* add model initialize calls
* add in build_tagger_model
* small fixes
* setting model dims
* fixes for ParserModel
* various small fixes
* initialize thinc Models
* fixes
* consistent naming of window_size
* fixes, removing set_dropout
* work around Iterable issue
* remove legacy tok2vec
* util fix
* fix forward function of tok2vec listener
* more fixes
* trying to fix PrecomputableAffine (not succesful yet)
* alloc instead of allocate
* add morphologizer
* rename residual
* rename fixes
* Fix predict function
* Update parser and parser model
* fixing few more tests
* Fix precomputable affine
* Update component model
* Update parser model
* Move backprop padding to own function, for test
* Update test
* Fix p. affine
* Update NEL
* build_bow_text_classifier and extract_ngrams
* Fix parser init
* Fix test add label
* add build_simple_cnn_text_classifier
* Fix parser init
* Set gpu off by default in example
* Fix tok2vec listener
* Fix parser model
* Small fixes
* small fix for PyTorchLSTM parameters
* revert my_compounding hack (iterable fixed now)
* fix biLSTM
* Fix uniqued
* PyTorchRNNWrapper fix
* small fixes
* use helper function to calculate cosine loss
* small fixes for build_simple_cnn_text_classifier
* putting dropout default at 0.0 to ensure the layer gets built
* using thinc util's set_dropout_rate
* moving layer normalization inside of maxout definition to optimize dropout
* temp debugging in NEL
* fixed NEL model by using init defaults !
* fixing after set_dropout_rate refactor
* proper fix
* fix test_update_doc after refactoring optimizers in thinc
* Add CharacterEmbed layer
* Construct tagger Model
* Add missing import
* Remove unused stuff
* Work on textcat
* fix test (again :)) after optimizer refactor
* fixes to allow reading Tagger from_disk without overwriting dimensions
* don't build the tok2vec prematuraly
* fix CharachterEmbed init
* CharacterEmbed fixes
* Fix CharacterEmbed architecture
* fix imports
* renames from latest thinc update
* one more rename
* add initialize calls where appropriate
* fix parser initialization
* Update Thinc version
* Fix errors, auto-format and tidy up imports
* Fix validation
* fix if bias is cupy array
* revert for now
* ensure it's a numpy array before running bp in ParserStepModel
* no reason to call require_gpu twice
* use CupyOps.to_numpy instead of cupy directly
* fix initialize of ParserModel
* remove unnecessary import
* fixes for CosineDistance
* fix device renaming
* use refactored loss functions (Thinc PR 251)
* overfitting test for tagger
* experimental settings for the tagger: avoid zero-init and subword normalization
* clean up tagger overfitting test
* use previous default value for nP
* remove toy config
* bringing layernorm back (had a bug - fixed in thinc)
* revert setting nP explicitly
* remove setting default in constructor
* restore values as they used to be
* add overfitting test for NER
* add overfitting test for dep parser
* add overfitting test for textcat
* fixing init for linear (previously affine)
* larger eps window for textcat
* ensure doc is not None
* Require newer thinc
* Make float check vaguer
* Slop the textcat overfit test more
* Fix textcat test
* Fix exclusive classes for textcat
* fix after renaming of alloc methods
* fixing renames and mandatory arguments (staticvectors WIP)
* upgrade to thinc==8.0.0.dev3
* refer to vocab.vectors directly instead of its name
* rename alpha to learn_rate
* adding hashembed and staticvectors dropout
* upgrade to thinc 8.0.0.dev4
* add name back to avoid warning W020
* thinc dev4
* update srsly
* using thinc 8.0.0a0 !
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
Co-authored-by: Ines Montani <ines@ines.io>
* 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>
* 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
* 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
While developing v2.1, I ran a bunch of hyper-parameter search
experiments to find settings that performed well for spaCy's NER and
parser. I ended up changing the default Adam settings from beta1=0.9,
beta2=0.999, eps=1e-8 to beta1=0.8, beta2=0.8, eps=1e-5. This was giving
a small improvement in accuracy (like, 0.4%).
Months later, I run the models with Prodigy, which uses beam-search
decoding even when the model has been trained with a greedy objective.
The new models performed terribly...So, wtf? After a couple of days
debugging, I figured out that the new optimizer settings was causing the
model to converge to solutions where the top-scoring class often had
a score of like, -80. The variance on the weights had gone up
enormously. I guess I needed to update the L2 regularisation as well?
Anyway. Let's just revert the change --- if the optimizer is finding
such extreme solutions, that seems bad, and not nearly worth the small
improvement in accuracy.
Currently training a slate of models, to verify the accuracy change is minimal.
Once the training is complete, we can merge this.
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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
Currently the TextCategorizer defaults to a fairly complicated model, designed partly around the active learning requirements of Prodigy. The model's a bit slow, and not very GPU-friendly.
This patch implements a straightforward CNN model that still performs pretty well. The replacement model also makes it easy to use the LMAO pretraining, since most of the parameters are in the CNN.
The replacement model has a flag to specify whether labels are mutually exclusive, which defaults to True. This has been a common problem with the text classifier. We'll also now be able to support adding labels to pretrained models again.
Resolves#2934, #2756, #1798, #1748.
Sofie Van Landeghem