mirror of https://github.com/explosion/spaCy.git
114 lines
2.4 KiB
Cython
114 lines
2.4 KiB
Cython
from libc.stdint cimport int32_t, int64_t, uint8_t, uint32_t, uint64_t
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from libcpp.unordered_map cimport unordered_map
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from libcpp.unordered_set cimport unordered_set
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from libcpp.vector cimport vector
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from .parts_of_speech cimport univ_pos_t
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from .typedefs cimport attr_t, flags_t, hash_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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hash_t key
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int length
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attr_t* fields
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attr_t* features
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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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cdef struct EdgeC:
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hash_t label
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int32_t head
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int32_t tail
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cdef struct GraphC:
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vector[vector[int32_t]] nodes
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vector[EdgeC] edges
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vector[float] weights
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vector[int] n_heads
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vector[int] n_tails
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vector[int] first_head
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vector[int] first_tail
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unordered_set[int]* roots
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unordered_map[hash_t, int]* node_map
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unordered_map[hash_t, int]* edge_map
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