* 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
* typo fix
* add candidate API to kb documentation
* update API sidebar with EntityLinker and KnowledgeBase
* remove EL from 101 docs
* remove entity linker from 101 pipelines / rephrase
* custom el model instead of existing model
* set version to 2.2 for EL functionality
* update documentation for 2 CLI scripts
* 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
* Fix typo in rule-based matching docs
* Improve token pattern checking without validation
Add more detailed token pattern checks without full JSON pattern validation and
provide more detailed error messages.
Addresses #4070 (also related: #4063, #4100).
* Check whether top-level attributes in patterns and attr for PhraseMatcher are
in token pattern schema
* Check whether attribute value types are supported in general (as opposed to
per attribute with full validation)
* Report various internal error types (OverflowError, AttributeError, KeyError)
as ValueError with standard error messages
* Check for tagger/parser in PhraseMatcher pipeline for attributes TAG, POS,
LEMMA, and DEP
* Add error messages with relevant details on how to use validate=True or nlp()
instead of nlp.make_doc()
* Support attr=TEXT for PhraseMatcher
* Add NORM to schema
* Expand tests for pattern validation, Matcher, PhraseMatcher, and EntityRuler
* Remove unnecessary .keys()
* Rephrase error messages
* Add another type check to Matcher
Add another type check to Matcher for more understandable error messages
in some rare cases.
* Support phrase_matcher_attr=TEXT for EntityRuler
* Don't use spacy.errors in examples and bin scripts
* Fix error code
* Auto-format
Also try get Azure pipelines to finally start a build :(
* Update errors.py
Co-authored-by: Ines Montani <ines@ines.io>
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Added RONEC to spaCy Universe
* Added contributor file
* Corrected date from .github/contributors/avramandrei.md
* Convert tabs to spaces
* Remove duplicate keys
Can only have one GitHub link unfortunately
* Also add models category
* Adjust ID
This is used to generate the URL, so a simpler string is better
* Add entry for Blackstone in universe.json
Add an entry for the Blackstone project. Checked JSON is valid.
* Create ICLRandD.md
* Fix indentation (tabs to spaces)
It looks like during validation, the JSON file automatically changed spaces to tabs. This caused the diff to show *everything* as changed, which is obviously not true. This hopefully fixes that.
* Try to fix formatting for diff
* Fix diff
Co-authored-by: Ines Montani <ines@ines.io>
* Typo fix for AllenAI url
Changed incorrect home page url for AllenAI from appenai.org to allenai.org
* Sign contributor agreement
* Change date format
* add `words`
* update name of entity list to `ner`
I think it might be a bit more consistent to have `ner` named `entities`
or `ents` (and `ents` is actually set somewhere to `None`, which is a
bit confusing), but it looks like renaming it would be a non-trivial
decision.
* 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
* Perserve flags in EntityRuler
The EntityRuler (explosion/spaCy#3526) does not preserve
overwrite flags (or `ent_id_sep`) when serialized. This
commit adds support for serialization/deserialization preserving
overwrite and ent_id_sep flags.
* add signed contributor agreement
* flake8 cleanup
mostly blank line issues.
* mark test from the issue as needing a model
The test from the issue needs some language model for serialization
but the test wasn't originally marked correctly.
* Adds `phrase_matcher_attr` to allow args to PhraseMatcher
This is an added arg to pass to the `PhraseMatcher`. For example,
this allows creation of a case insensitive phrase matcher when the
`EntityRuler` is created. References explosion/spaCy#3822
* remove unneeded model loading
The model didn't need to be loaded, and I replaced it with
a change that doesn't require it (using existings fixtures)
* updated docstring for new argument
* updated docs to reflect new argument to the EntityRuler constructor
* change tempdir handling to be compatible with python 2.7
* return conflicted code to entityruler
Some stuff got cut out because of merge conflicts, this
returns that code for the phrase_matcher_attr.
* fixed typo in the code added back after conflicts
* flake8 compliance
When I deconflicted the branch there were some flake8 issues
introduced. This resolves the spacing problems.
* test changes: attempts to fix flaky test in python3.5
These tests seem to be alittle flaky in 3.5 so I changed the check to avoid
the comparisons that seem to be fail sometimes.
* 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
* Changed learning rate by its param name.
I've been searching for a while how the parameter learning rate was named, with `beta1` and `beta2` its easy as they are marked as code, but learning rate wasn't. I think writing the actual parameter name would be helpful.
* Signing SCA
* Update tokenizer.md for construction example
Self contained example. You should really say what nlp is so that the example will work as is
* Update CONTRIBUTOR_AGREEMENT.md
* Restore contributor agreement
* Adjust construction examples
* 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
* Request to add Holmes to spaCy Universe
Dear spaCy team, I would be grateful if you would consider my Python library Holmes for inclusion in the spaCy Universe. Holmes transforms the syntactic structures delivered by spaCy into semantic structures that, together with various other techniques including ontological matching and word embeddings, serve as the basis for information extraction. Holmes supports several use cases including chatbot, structured search, topic matching and supervised document classification. I had the basic idea for Holmes around 15 years ago and now spaCy has made it possible to build an implementation that is stable and fast enough to actually be of use - thank you! At present Holmes supports English and German (I am based in Munich) but could easily be extended to support any other language with a spaCy model.
* Added
<!--- 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.
As discussed with Ines in https://github.com/explosion/spaCy/issues/3568 , adding a new project proposal for the community in SpaCy Universe website
GracyQL a tiny graphql wrapper aroung spacy using graphene and starlette.
## Description
Change only in universe.json file to add a new project
### Types of change
New project reference in Universe
## Checklist
- [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.
* fix(util): fix decaying function output
* fix(util): better test and adhere to code standards
* fix(util): correct variable name, pytestify test, update website text
* 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
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.
