mirror of
https://github.com/explosion/spaCy.git
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a5cd203284
* Reduce stored lexemes data, move feats to lookups
* Move non-derivable lexemes features (`norm / cluster / prob`) to
`spacy-lookups-data` as lookups
* Get/set `norm` in both lookups and `LexemeC`, serialize in lookups
* Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in
lookups only
* Remove serialization of lexemes data as `vocab/lexemes.bin`
* Remove `SerializedLexemeC`
* Remove `Lexeme.to_bytes/from_bytes`
* Modify normalization exception loading:
* Always create `Vocab.lookups` table `lexeme_norm` for
normalization exceptions
* Load base exceptions from `lang.norm_exceptions`, but load
language-specific exceptions from lookups
* Set `lex_attr_getter[NORM]` including new lookups table in
`BaseDefaults.create_vocab()` and when deserializing `Vocab`
* Remove all cached lexemes when deserializing vocab to override
existing normalizations with the new normalizations (as a replacement
for the previous step that replaced all lexemes data with the
deserialized data)
* Skip English normalization test
Skip English normalization test because the data is now in
`spacy-lookups-data`.
* Remove norm exceptions
Moved to spacy-lookups-data.
* Move norm exceptions test to spacy-lookups-data
* Load extra lookups from spacy-lookups-data lazily
Load extra lookups (currently for cluster and prob) lazily from the
entry point `lg_extra` as `Vocab.lookups_extra`.
* Skip creating lexeme cache on load
To improve model loading times, do not create the full lexeme cache when
loading. The lexemes will be created on demand when processing.
* Identify numeric values in Lexeme.set_attrs()
With the removal of a special case for `PROB`, also identify `float` to
avoid trying to convert it with the `StringStore`.
* Skip lexeme cache init in from_bytes
* Unskip and update lookups tests for python3.6+
* Update vocab pickle to include lookups_extra
* Update vocab serialization tests
Check strings rather than lexemes since lexemes aren't initialized
automatically, account for addition of "_SP".
* Re-skip lookups test because of python3.5
* Skip PROB/float values in Lexeme.set_attrs
* Convert is_oov from lexeme flag to lex in vectors
Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether
the lexeme has a vector.
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
136 lines
2.8 KiB
Cython
136 lines
2.8 KiB
Cython
from libc.stdint cimport uint8_t, uint32_t, int32_t, uint64_t
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from .typedefs cimport flags_t, attr_t, hash_t
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from .parts_of_speech cimport univ_pos_t
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from libcpp.vector cimport vector
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from libc.stdint cimport int32_t, int64_t
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cdef struct LexemeC:
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flags_t flags
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attr_t lang
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attr_t id
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attr_t length
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attr_t orth
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attr_t lower
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attr_t norm
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attr_t shape
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attr_t prefix
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attr_t suffix
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cdef struct SpanC:
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hash_t id
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int start
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int end
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int start_char
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int end_char
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attr_t label
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attr_t kb_id
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cdef struct TokenC:
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const LexemeC* lex
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uint64_t morph
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univ_pos_t pos
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bint spacy
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attr_t tag
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int idx
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attr_t lemma
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attr_t norm
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int head
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attr_t dep
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uint32_t l_kids
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uint32_t r_kids
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uint32_t l_edge
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uint32_t r_edge
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int sent_start
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int ent_iob
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attr_t ent_type # TODO: Is there a better way to do this? Multiple sources of truth..
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attr_t ent_kb_id
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hash_t ent_id
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cdef struct MorphAnalysisC:
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univ_pos_t pos
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int length
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attr_t abbr
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attr_t adp_type
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attr_t adv_type
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attr_t animacy
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attr_t aspect
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attr_t case
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attr_t conj_type
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attr_t connegative
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attr_t definite
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attr_t degree
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attr_t derivation
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attr_t echo
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attr_t foreign
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attr_t gender
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attr_t hyph
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attr_t inf_form
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attr_t mood
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attr_t negative
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attr_t number
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attr_t name_type
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attr_t noun_type
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attr_t num_form
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attr_t num_type
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attr_t num_value
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attr_t part_form
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attr_t part_type
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attr_t person
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attr_t polite
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attr_t polarity
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attr_t poss
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attr_t prefix
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attr_t prep_case
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attr_t pron_type
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attr_t punct_side
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attr_t punct_type
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attr_t reflex
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attr_t style
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attr_t style_variant
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attr_t tense
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attr_t typo
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attr_t verb_form
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attr_t voice
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attr_t verb_type
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# Internal struct, for storage and disambiguation of entities.
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cdef struct KBEntryC:
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# The hash of this entry's unique ID/name in the kB
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hash_t entity_hash
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# Allows retrieval of the entity vector, as an index into a vectors table of the KB.
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# Can be expanded later to refer to multiple rows (compositional model to reduce storage footprint).
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int32_t vector_index
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# Allows retrieval of a struct of non-vector features.
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# This is currently not implemented and set to -1 for the common case where there are no features.
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int32_t feats_row
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# log probability of entity, based on corpus frequency
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float freq
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# Each alias struct stores a list of Entry pointers with their prior probabilities
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# for this specific mention/alias.
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cdef struct AliasC:
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# All entry candidates for this alias
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vector[int64_t] entry_indices
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# Prior probability P(entity|alias) - should sum up to (at most) 1.
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vector[float] probs
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