* 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>
* Flag to ignore examples with mismatched raw/gold text
After #4525, we're seeing some alignment failures on our OntoNotes data. I think we actually have fixes for most of these cases.
In general it's better to fix the data, but it seems good to allow the GoldCorpus class to just skip cases where the raw text doesn't
match up to the gold words. I think previously we were silently ignoring these cases.
* Try to fix test on Python 2.7
* Error for ill-formed input to iob_to_biluo()
Check for empty label in iob_to_biluo(), which can result from
ill-formed input.
* Check for empty NER label in debug-data
* 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
* Only import pkg_resources where it's needed
Apparently it's really slow
* Use importlib_metadata for entry points
* Revert "Only import pkg_resources where it's needed"
This reverts commit 5ed8c03afa.
* Revert "Revert "Only import pkg_resources where it's needed""
This reverts commit 8b30b57957.
* Revert "Use importlib_metadata for entry points"
This reverts commit 9f071f5c40.
* Revert "Revert "Use importlib_metadata for entry points""
This reverts commit 02e12a17ec.
* Skip test that weirdly hangs
* Fix hanging test by using global
* Allow vectors name to be specified in init-model
* Document --vectors-name argument to init-model
* Update website/docs/api/cli.md
Co-Authored-By: Ines Montani <ines@ines.io>
* 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>
* Updates/bugfixes for NER/IOB converters
* Converter formats `ner` and `iob` use autodetect to choose a converter if
possible
* `iob2json` is reverted to handle sentence-per-line data like
`word1|pos1|ent1 word2|pos2|ent2`
* Fix bug in `merge_sentences()` so the second sentence in each batch isn't
skipped
* `conll_ner2json` is made more general so it can handle more formats with
whitespace-separated columns
* Supports all formats where the first column is the token and the final
column is the IOB tag; if present, the second column is the POS tag
* As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O`
separates documents
* Add option for segmenting sentences (new flag `-s`)
* Parser-based sentence segmentation with a provided model, otherwise with
sentencizer (new option `-b` to specify model)
* Can group sentences into documents with `n_sents` as long as sentence
segmentation is available
* Only applies automatic segmentation when there are no existing delimiters
in the data
* Provide info about settings applied during conversion with warnings and
suggestions if settings conflict or might not be not optimal.
* Add tests for common formats
* Add '(default)' back to docs for -c auto
* Add document count back to output
* Revert changes to converter output message
* Use explicit tabs in convert CLI test data
* Adjust/add messages for n_sents=1 default
* Add sample NER data to training examples
* Update README
* Add links in docs to example NER data
* Define msg within converters
* 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
* Extending debug-data with dependency checks, etc.
* Modify debug-data to load with GoldCorpus to iterate over .json/.jsonl
files within directories
* Add GoldCorpus iterator train_docs_without_preprocessing to load
original train docs without shuffling and projectivizing
* Report number of misaligned tokens
* Add more dependency checks and messages
* Update spacy/cli/debug_data.py
Co-Authored-By: Ines Montani <ines@ines.io>
* Fixed conflict
* Move counts to _compile_gold()
* Move all dependency nonproj/sent/head/cycle counting to
_compile_gold()
* Unclobber previous merges
* Update variable names
* Update more variable names, fix misspelling
* Don't clobber loading error messages
* Only warn about misaligned tokens if present
* Update pretrain to prevent unintended overwriting of weight files for #3859
* Add '--epoch-start' to pretrain docs
* Add mising pretrain arguments to bash example
* Update doc tag for v2.1.5
* Add error to `get_vectors_loss` for unsupported loss function of `pretrain`
* Add missing "--loss-func" argument to pretrain docs. Update pretrain plac annotations to match docs.
* Add missing quotation marks
* Add check for empty input file to CLI pretrain
* Raise error if JSONL is not a dict or contains neither `tokens` nor `text` key
* Skip empty values for correct pretrain keys and log a counter as warning
* Add tests for CLI pretrain core function make_docs.
* Add a short hint for the `tokens` key to the CLI pretrain docs
* Add success message to CLI pretrain
* Update model loading to fix the tests
* Skip empty values and do not create docs out of it
<!--- Provide a general summary of your changes in the title. -->
When using `spacy pretrain`, the model is saved only after every epoch. But each epoch can be very big since `pretrain` is used for language modeling tasks. So I added a `--save-every` option in the CLI to save after every `--save-every` batches.
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
To test...
Save this file to `sample_sents.jsonl`
```
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
```
Then run `--save-every 2` when pretraining.
```bash
spacy pretrain sample_sents.jsonl en_core_web_md here -nw 1 -bs 1 -i 10 --save-every 2
```
And it should save the model to the `here/` folder after every 2 batches. The models that are saved during an epoch will have a `.temp` appended to the save name.
At the end the training, you should see these files (`ls here/`):
```bash
config.json model2.bin model5.bin model8.bin
log.jsonl model2.temp.bin model5.temp.bin model8.temp.bin
model0.bin model3.bin model6.bin model9.bin
model0.temp.bin model3.temp.bin model6.temp.bin model9.temp.bin
model1.bin model4.bin model7.bin
model1.temp.bin model4.temp.bin model7.temp.bin
```
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
This is a new feature to `spacy pretrain`.
🌵 **Unfortunately, I haven't been able to test this because compiling from source is not working (cythonize error).**
```
Processing matcher.pyx
[Errno 2] No such file or directory: '/Users/mwu/github/spaCy/spacy/matcher.pyx'
Traceback (most recent call last):
File "/Users/mwu/github/spaCy/bin/cythonize.py", line 169, in <module>
run(args.root)
File "/Users/mwu/github/spaCy/bin/cythonize.py", line 158, in run
process(base, filename, db)
File "/Users/mwu/github/spaCy/bin/cythonize.py", line 124, in process
preserve_cwd(base, process_pyx, root + ".pyx", root + ".cpp")
File "/Users/mwu/github/spaCy/bin/cythonize.py", line 87, in preserve_cwd
func(*args)
File "/Users/mwu/github/spaCy/bin/cythonize.py", line 63, in process_pyx
raise Exception("Cython failed")
Exception: Cython failed
Traceback (most recent call last):
File "setup.py", line 276, in <module>
setup_package()
File "setup.py", line 209, in setup_package
generate_cython(root, "spacy")
File "setup.py", line 132, in generate_cython
raise RuntimeError("Running cythonize failed")
RuntimeError: Running cythonize failed
```
Edit: Fixed! after deleting all `.cpp` files: `find spacy -name "*.cpp" | xargs rm`
## 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.
* Add early stopping
* Add return_score option to evaluate
* Fix missing str to path conversion
* Fix import + old python compatibility
* Fix bad beam_width setting during cpu evaluation in spacy train with gpu option turned on
Add and document CLI options for batch size, max doc length, min doc length for `spacy pretrain`.
Also improve CLI output.
Closes#3216
## 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.
* merging conllu/conll and conllubio scripts
* tabs to spaces
* removing conllubio2json from converters/__init__.py
* Move not-really-CLI tests to misc
* Add converter test using no-ud data
* Fix test I broke
* removing include_biluo parameter
* fixing read_conllx
* remove include_biluo from convert.py
* Add custom MatchPatternError
* Improve validators and add validation option to Matcher
* Adjust formatting
* Never validate in Matcher within PhraseMatcher
If we do decide to make validate default to True, the PhraseMatcher's Matcher shouldn't ever validate. Here, we create the patterns automatically anyways (and it's currently unclear whether the validation has performance impacts at a very large scale).
* running UD eval
* printing timing of tokenizer: tokens per second
* timing of default English model
* structured output and parameterization to compare different runs
* additional flag to allow evaluation without parsing info
* printing verbose log of errors for manual inspection
* printing over- and undersegmented cases (and combo's)
* add under and oversegmented numbers to Score and structured output
* print high-freq over/under segmented words and word shapes
* printing examples as part of the structured output
* print the results to file
* batch run of different models and treebanks per language
* cleaning up code
* commandline script to process all languages in spaCy & UD
* heuristic to remove blinded corpora and option to run one single best per language
* pathlib instead of os for file paths
