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* Create aryaprabhudesai.md (#2681) * Update _install.jade (#2688) Typo fix: "models" -> "model" * Add FAC to spacy.explain (resolves #2706) * Remove docstrings for deprecated arguments (see #2703) * When calling getoption() in conftest.py, pass a default option (#2709) * When calling getoption() in conftest.py, pass a default option This is necessary to allow testing an installed spacy by running: pytest --pyargs spacy * Add contributor agreement * update bengali token rules for hyphen and digits (#2731) * Less norm computations in token similarity (#2730) * Less norm computations in token similarity * Contributor agreement * Remove ')' for clarity (#2737) Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know. * added contributor agreement for mbkupfer (#2738) * Basic support for Telugu language (#2751) * Lex _attrs for polish language (#2750) * Signed spaCy contributor agreement * Added polish version of english lex_attrs * Introduces a bulk merge function, in order to solve issue #653 (#2696) * Fix comment * Introduce bulk merge to increase performance on many span merges * Sign contributor agreement * Implement pull request suggestions * Describe converters more explicitly (see #2643) * Add multi-threading note to Language.pipe (resolves #2582) [ci skip] * Fix formatting * Fix dependency scheme docs (closes #2705) [ci skip] * Don't set stop word in example (closes #2657) [ci skip] * Add words to portuguese language _num_words (#2759) * Add words to portuguese language _num_words * Add words to portuguese language _num_words * Update Indonesian model (#2752) * adding e-KTP in tokenizer exceptions list * add exception token * removing lines with containing space as it won't matter since we use .split() method in the end, added new tokens in exception * add tokenizer exceptions list * combining base_norms with norm_exceptions * adding norm_exception * fix double key in lemmatizer * remove unused import on punctuation.py * reformat stop_words to reduce number of lines, improve readibility * updating tokenizer exception * implement is_currency for lang/id * adding orth_first_upper in tokenizer_exceptions * update the norm_exception list * remove bunch of abbreviations * adding contributors file * Fixed spaCy+Keras example (#2763) * bug fixes in keras example * created contributor agreement * Adding French hyphenated first name (#2786) * Fix typo (closes #2784) * Fix typo (#2795) [ci skip] Fixed typo on line 6 "regcognizer --> recognizer" * Adding basic support for Sinhala language. (#2788) * adding Sinhala language package, stop words, examples and lex_attrs. * Adding contributor agreement * Updating contributor agreement * Also include lowercase norm exceptions * Fix error (#2802) * Fix error ValueError: cannot resize an array that references or is referenced by another array in this way. Use the resize function * added spaCy Contributor Agreement * Add charlax's contributor agreement (#2805) * agreement of contributor, may I introduce a tiny pl languge contribution (#2799) * Contributors agreement * Contributors agreement * Contributors agreement * Add jupyter=True to displacy.render in documentation (#2806) * Revert "Also include lowercase norm exceptions" This reverts commit70f4e8adf3
. * Remove deprecated encoding argument to msgpack * Set up dependency tree pattern matching skeleton (#2732) * Fix bug when too many entity types. Fixes #2800 * Fix Python 2 test failure * Require older msgpack-numpy * Restore encoding arg on msgpack-numpy * Try to fix version pin for msgpack-numpy * Update Portuguese Language (#2790) * Add words to portuguese language _num_words * Add words to portuguese language _num_words * Portuguese - Add/remove stopwords, fix tokenizer, add currency symbols * Extended punctuation and norm_exceptions in the Portuguese language * Correct error in spacy universe docs concerning spacy-lookup (#2814) * Update Keras Example for (Parikh et al, 2016) implementation (#2803) * bug fixes in keras example * created contributor agreement * baseline for Parikh model * initial version of parikh 2016 implemented * tested asymmetric models * fixed grevious error in normalization * use standard SNLI test file * begin to rework parikh example * initial version of running example * start to document the new version * start to document the new version * Update Decompositional Attention.ipynb * fixed calls to similarity * updated the README * import sys package duh * simplified indexing on mapping word to IDs * stupid python indent error * added code from https://github.com/tensorflow/tensorflow/issues/3388 for tf bug workaround * Fix typo (closes #2815) [ci skip] * Update regex version dependency * Set version to 2.0.13.dev3 * Skip seemingly problematic test * Remove problematic test * Try previous version of regex * Revert "Remove problematic test" This reverts commitbdebbef455
. * Unskip test * Try older version of regex * 💫 Update training examples and use minibatching (#2830) <!--- Provide a general summary of your changes in the title. --> ## Description Update the training examples in `/examples/training` to show usage of spaCy's `minibatch` and `compounding` helpers ([see here](https://spacy.io/usage/training#tips-batch-size) for details). The lack of batching in the examples has caused some confusion in the past, especially for beginners who would copy-paste the examples, update them with large training sets and experienced slow and unsatisfying results. ### Types of change enhancements ## 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. * Visual C++ link updated (#2842) (closes #2841) [ci skip] * New landing page * Add contribution agreement * Correcting lang/ru/examples.py (#2845) * Correct some grammatical inaccuracies in lang\ru\examples.py; filled Contributor Agreement * Correct some grammatical inaccuracies in lang\ru\examples.py * Move contributor agreement to separate file * Set version to 2.0.13.dev4 * Add Persian(Farsi) language support (#2797) * Also include lowercase norm exceptions * Remove in favour of https://github.com/explosion/spaCy/graphs/contributors * Rule-based French Lemmatizer (#2818) <!--- Provide a general summary of your changes in the title. --> ## 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. --> Add a rule-based French Lemmatizer following the english one and the excellent PR for [greek language optimizations](https://github.com/explosion/spaCy/pull/2558) to adapt the Lemmatizer class. ### 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? --> - Lemma dictionary used can be found [here](http://infolingu.univ-mlv.fr/DonneesLinguistiques/Dictionnaires/telechargement.html), I used the XML version. - Add several files containing exhaustive list of words for each part of speech - Add some lemma rules - Add POS that are not checked in the standard Lemmatizer, i.e PRON, DET, ADV and AUX - Modify the Lemmatizer class to check in lookup table as a last resort if POS not mentionned - Modify the lemmatize function to check in lookup table as a last resort - Init files are updated so the model can support all the functionalities mentioned above - Add words to tokenizer_exceptions_list.py in respect to regex used in tokenizer_exceptions.py ## 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. * Set version to 2.0.13 * Fix formatting and consistency * Update docs for new version [ci skip] * Increment version [ci skip] * Add info on wheels [ci skip] * Adding "This is a sentence" example to Sinhala (#2846) * Add wheels badge * Update badge [ci skip] * Update README.rst [ci skip] * Update murmurhash pin * Increment version to 2.0.14.dev0 * Update GPU docs for v2.0.14 * Add wheel to setup_requires * Import prefer_gpu and require_gpu functions from Thinc * Add tests for prefer_gpu() and require_gpu() * Update requirements and setup.py * Workaround bug in thinc require_gpu * Set version to v2.0.14 * Update push-tag script * Unhack prefer_gpu * Require thinc 6.10.6 * Update prefer_gpu and require_gpu docs [ci skip] * Fix specifiers for GPU * Set version to 2.0.14.dev1 * Set version to 2.0.14 * Update Thinc version pin * Increment version * Fix msgpack-numpy version pin * Increment version * Update version to 2.0.16 * Update version [ci skip] * Redundant ')' in the Stop words' example (#2856) <!--- Provide a general summary of your changes in the title. --> ## 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. --> ### 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? --> ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [ ] I have submitted the spaCy Contributor Agreement. - [ ] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information. * Documentation improvement regarding joblib and SO (#2867) Some documentation improvements ## Description 1. Fixed the dead URL to joblib 2. Fixed Stack Overflow brand name (with space) ### Types of change Documentation ## 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. * raise error when setting overlapping entities as doc.ents (#2880) * Fix out-of-bounds access in NER training The helper method state.B(1) gets the index of the first token of the buffer, or -1 if no such token exists. Normally this is safe because we pass this to functions like state.safe_get(), which returns an empty token. Here we used it directly as an array index, which is not okay! This error may have been the cause of out-of-bounds access errors during training. Similar errors may still be around, so much be hunted down. Hunting this one down took a long time...I printed out values across training runs and diffed, looking for points of divergence between runs, when no randomness should be allowed. * Change PyThaiNLP Url (#2876) * Fix missing comma * Add example showing a fix-up rule for space entities * Set version to 2.0.17.dev0 * Update regex version * Revert "Update regex version" This reverts commit62358dd867
. * Try setting older regex version, to align with conda * Set version to 2.0.17 * Add spacy-js to universe [ci-skip] * Add spacy-raspberry to universe (closes #2889) * Add script to validate universe json [ci skip] * Removed space in docs + added contributor indo (#2909) * - removed unneeded space in documentation * - added contributor info * Allow input text of length up to max_length, inclusive (#2922) * Include universe spec for spacy-wordnet component (#2919) * feat: include universe spec for spacy-wordnet component * chore: include spaCy contributor agreement * Minor formatting changes [ci skip] * Fix image [ci skip] Twitter URL doesn't work on live site * Check if the word is in one of the regular lists specific to each POS (#2886) * 💫 Create random IDs for SVGs to prevent ID clashes (#2927) Resolves #2924. ## Description Fixes problem where multiple visualizations in Jupyter notebooks would have clashing arc IDs, resulting in weirdly positioned arc labels. Generating a random ID prefix so even identical parses won't receive the same IDs for consistency (even if effect of ID clash isn't noticable here.) ### Types of change bug fix ## 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. * Fix typo [ci skip] * fixes symbolic link on py3 and windows (#2949) * fixes symbolic link on py3 and windows during setup of spacy using command python -m spacy link en_core_web_sm en closes #2948 * Update spacy/compat.py Co-Authored-By: cicorias <cicorias@users.noreply.github.com> * Fix formatting * Update universe [ci skip] * Catalan Language Support (#2940) * Catalan language Support * Ddding Catalan to documentation * Sort languages alphabetically [ci skip] * Update tests for pytest 4.x (#2965) <!--- Provide a general summary of your changes in the title. --> ## Description - [x] Replace marks in params for pytest 4.0 compat ([see here](https://docs.pytest.org/en/latest/deprecations.html#marks-in-pytest-mark-parametrize)) - [x] Un-xfail passing tests (some fixes in a recent update resolved a bunch of issues, but tests were apparently never updated here) ### 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? --> ## 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. * Fix regex pin to harmonize with conda (#2964) * Update README.rst * Fix bug where Vocab.prune_vector did not use 'batch_size' (#2977) Fixes #2976 * Fix typo * Fix typo * Remove duplicate file * Require thinc 7.0.0.dev2 Fixes bug in gpu_ops that would use cupy instead of numpy on CPU * Add missing import * Fix error IDs * Fix tests
385 lines
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385 lines
9.4 KiB
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//- 💫 DOCS > API > LEXEME
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include ../_includes/_mixins
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p
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| An entry in the vocabulary. A #[code Lexeme] has no string context – it's
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| a word type, as opposed to a word token. It therefore has no
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| part-of-speech tag, dependency parse, or lemma (if lemmatization depends
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| on the part-of-speech tag).
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+h(2, "init") Lexeme.__init__
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+tag method
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p Create a #[code Lexeme] object.
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell The parent vocabulary.
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+row
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+cell #[code orth]
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+cell int
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+cell The orth id of the lexeme.
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+row("foot")
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+cell returns
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+cell #[code Lexeme]
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+cell The newly constructed object.
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+h(2, "set_flag") Lexeme.set_flag
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+tag method
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p Change the value of a boolean flag.
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+aside-code("Example").
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COOL_FLAG = nlp.vocab.add_flag(lambda text: False)
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nlp.vocab[u'spaCy'].set_flag(COOL_FLAG, True)
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to set.
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+row
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+cell #[code value]
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+cell bool
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+cell The new value of the flag.
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+h(2, "check_flag") Lexeme.check_flag
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+tag method
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p Check the value of a boolean flag.
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+aside-code("Example").
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is_my_library = lambda text: text in ['spaCy', 'Thinc']
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MY_LIBRARY = nlp.vocab.add_flag(is_my_library)
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assert nlp.vocab[u'spaCy'].check_flag(MY_LIBRARY) == True
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code flag_id]
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+cell int
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+cell The attribute ID of the flag to query.
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+row("foot")
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+cell returns
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+cell bool
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+cell The value of the flag.
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+h(2, "similarity") Lexeme.similarity
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+tag method
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+tag-model("vectors")
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p Compute a semantic similarity estimate. Defaults to cosine over vectors.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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orange = nlp.vocab[u'orange']
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apple_orange = apple.similarity(orange)
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orange_apple = orange.similarity(apple)
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assert apple_orange == orange_apple
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+table(["Name", "Type", "Description"])
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+row
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+cell other
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+cell -
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+cell
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| The object to compare with. By default, accepts #[code Doc],
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| #[code Span], #[code Token] and #[code Lexeme] objects.
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+row("foot")
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+cell returns
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+cell float
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+cell A scalar similarity score. Higher is more similar.
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+h(2, "has_vector") Lexeme.has_vector
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+tag property
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+tag-model("vectors")
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p
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| A boolean value indicating whether a word vector is associated with the
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| lexeme.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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assert apple.has_vector
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell returns
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+cell bool
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+cell Whether the lexeme has a vector data attached.
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+h(2, "vector") Lexeme.vector
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+tag property
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+tag-model("vectors")
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p A real-valued meaning representation.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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assert apple.vector.dtype == 'float32'
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assert apple.vector.shape == (300,)
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell returns
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+cell #[code.u-break numpy.ndarray[ndim=1, dtype='float32']]
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+cell A 1D numpy array representing the lexeme's semantics.
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+h(2, "vector_norm") Lexeme.vector_norm
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+tag property
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+tag-model("vectors")
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p The L2 norm of the lexeme's vector representation.
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+aside-code("Example").
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apple = nlp.vocab[u'apple']
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pasta = nlp.vocab[u'pasta']
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apple.vector_norm # 7.1346845626831055
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pasta.vector_norm # 7.759851932525635
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assert apple.vector_norm != pasta.vector_norm
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+table(["Name", "Type", "Description"])
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+row("foot")
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+cell returns
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+cell float
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+cell The L2 norm of the vector representation.
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+h(2, "attributes") Attributes
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+table(["Name", "Type", "Description"])
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+row
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+cell #[code vocab]
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+cell #[code Vocab]
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+cell The lexeme's vocabulary.
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+row
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+cell #[code text]
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+cell unicode
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+cell Verbatim text content.
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+row
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+cell #[code orth]
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+cell int
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+cell ID of the verbatim text content.
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+row
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+cell #[code orth_]
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+cell unicode
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+cell
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| Verbatim text content (identical to #[code Lexeme.text]). Exists
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| mostly for consistency with the other attributes.
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+row
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+cell #[code lex_id]
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+cell int
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+cell ID of the lexeme's lexical type.
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+row
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+cell #[code rank]
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+cell int
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+cell
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| Sequential ID of the lexemes's lexical type, used to index into
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| tables, e.g. for word vectors.
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+row
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+cell #[code flags]
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+cell int
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+cell Container of the lexeme's binary flags.
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+row
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+cell #[code norm]
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+cell int
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+cell The lexemes's norm, i.e. a normalised form of the lexeme text.
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+row
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+cell #[code norm_]
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+cell unicode
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+cell The lexemes's norm, i.e. a normalised form of the lexeme text.
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+row
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+cell #[code lower]
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+cell int
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+cell Lowercase form of the word.
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+row
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+cell #[code lower_]
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+cell unicode
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+cell Lowercase form of the word.
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+row
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+cell #[code shape]
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+cell int
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+cell Transform of the word's string, to show orthographic features.
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+row
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+cell #[code shape_]
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+cell unicode
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+cell Transform of the word's string, to show orthographic features.
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+row
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+cell #[code prefix]
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+cell int
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+cell
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| Length-N substring from the start of the word. Defaults to
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| #[code N=1].
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+row
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+cell #[code prefix_]
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+cell unicode
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+cell
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| Length-N substring from the start of the word. Defaults to
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| #[code N=1].
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+row
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+cell #[code suffix]
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+cell int
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+cell
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| Length-N substring from the end of the word. Defaults to
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| #[code N=3].
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+row
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+cell #[code suffix_]
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+cell unicode
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+cell
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| Length-N substring from the start of the word. Defaults to
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| #[code N=3].
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+row
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+cell #[code is_alpha]
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+cell bool
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+cell
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| Does the lexeme consist of alphabetic characters? Equivalent to
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| #[code lexeme.text.isalpha()].
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+row
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+cell #[code is_ascii]
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+cell bool
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+cell
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| Does the lexeme consist of ASCII characters? Equivalent to
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| #[code [any(ord(c) >= 128 for c in lexeme.text)]].
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+row
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+cell #[code is_digit]
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+cell bool
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+cell
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| Does the lexeme consist of digits? Equivalent to
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| #[code lexeme.text.isdigit()].
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+row
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+cell #[code is_lower]
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+cell bool
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+cell
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| Is the lexeme in lowercase? Equivalent to
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| #[code lexeme.text.islower()].
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+row
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+cell #[code is_upper]
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+cell bool
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+cell
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| Is the lexeme in uppercase? Equivalent to
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| #[code lexeme.text.isupper()].
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+row
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+cell #[code is_title]
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+cell bool
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+cell
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| Is the lexeme in titlecase? Equivalent to
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| #[code lexeme.text.istitle()].
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+row
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+cell #[code is_punct]
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+cell bool
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+cell Is the lexeme punctuation?
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+row
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+cell #[code is_left_punct]
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+cell bool
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+cell Is the lexeme a left punctuation mark, e.g. #[code (]?
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+row
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+cell #[code is_right_punct]
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+cell bool
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+cell Is the lexeme a right punctuation mark, e.g. #[code )]?
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+row
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+cell #[code is_space]
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+cell bool
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||
+cell
|
||
| Does the lexeme consist of whitespace characters? Equivalent to
|
||
| #[code lexeme.text.isspace()].
|
||
|
||
+row
|
||
+cell #[code is_bracket]
|
||
+cell bool
|
||
+cell Is the lexeme a bracket?
|
||
|
||
+row
|
||
+cell #[code is_quote]
|
||
+cell bool
|
||
+cell Is the lexeme a quotation mark?
|
||
|
||
+row
|
||
+cell #[code is_currency]
|
||
+tag-new("2.0.8")
|
||
+cell bool
|
||
+cell Is the lexeme a currency symbol?
|
||
|
||
+row
|
||
+cell #[code like_url]
|
||
+cell bool
|
||
+cell Does the lexeme resemble a URL?
|
||
|
||
+row
|
||
+cell #[code like_num]
|
||
+cell bool
|
||
+cell Does the lexeme represent a number? e.g. "10.9", "10", "ten", etc.
|
||
|
||
+row
|
||
+cell #[code like_email]
|
||
+cell bool
|
||
+cell Does the lexeme resemble an email address?
|
||
|
||
+row
|
||
+cell #[code is_oov]
|
||
+cell bool
|
||
+cell Is the lexeme out-of-vocabulary?
|
||
|
||
+row
|
||
+cell #[code is_stop]
|
||
+cell bool
|
||
+cell Is the lexeme part of a "stop list"?
|
||
|
||
+row
|
||
+cell #[code lang]
|
||
+cell int
|
||
+cell Language of the parent vocabulary.
|
||
|
||
+row
|
||
+cell #[code lang_]
|
||
+cell unicode
|
||
+cell Language of the parent vocabulary.
|
||
|
||
+row
|
||
+cell #[code prob]
|
||
+cell float
|
||
+cell Smoothed log probability estimate of the lexeme's type.
|
||
|
||
+row
|
||
+cell #[code cluster]
|
||
+cell int
|
||
+cell Brown cluster ID.
|
||
|
||
+row
|
||
+cell #[code sentiment]
|
||
+cell float
|
||
+cell
|
||
| A scalar value indicating the positivity or negativity of the
|
||
| lexeme.
|