mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
"""Thinc layer to do simpler transition-based parsing, NER, etc."""
|
|
from typing import Dict, Optional
|
|
from thinc.api import Ops, Model
|
|
from thinc.types import Padded, Floats3d
|
|
|
|
|
|
def IOB() -> Model[Padded, Padded]:
|
|
return Model(
|
|
"biluo",
|
|
forward,
|
|
init=init,
|
|
dims={"nO": None},
|
|
attrs={"get_num_actions": get_num_actions},
|
|
)
|
|
|
|
|
|
def init(model, X: Optional[Padded] = None, Y: Optional[Padded] = None):
|
|
if X is not None and Y is not None:
|
|
if X.data.shape != Y.data.shape:
|
|
# TODO: Fix error
|
|
raise ValueError("Mismatched shapes (TODO: Fix message)")
|
|
model.set_dim("nO", X.data.shape[2])
|
|
elif X is not None:
|
|
model.set_dim("nO", X.data.shape[2])
|
|
elif Y is not None:
|
|
model.set_dim("nO", Y.data.shape[2])
|
|
elif model.get_dim("nO") is None:
|
|
raise ValueError("Dimension unset for BILUO: nO")
|
|
|
|
|
|
def forward(model: Model[Padded, Padded], Xp: Padded, is_train: bool):
|
|
n_labels = (model.get_dim("nO") - 1) // 2
|
|
n_tokens, n_docs, n_actions = Xp.data.shape
|
|
# At each timestep, we make a validity mask of shape (n_docs, n_actions)
|
|
# to indicate which actions are valid next for each sequence. To construct
|
|
# the mask, we have a state of shape (2, n_actions) and a validity table of
|
|
# shape (2, n_actions+1, n_actions). The first dimension of the state indicates
|
|
# whether it's the last token, the second dimension indicates the previous
|
|
# action, plus a special 'null action' for the first entry.
|
|
valid_transitions = _get_transition_table(model.ops, n_labels)
|
|
prev_actions = model.ops.alloc1i(n_docs)
|
|
# Initialize as though prev action was O
|
|
prev_actions.fill(n_actions - 1)
|
|
Y = model.ops.alloc3f(*Xp.data.shape)
|
|
masks = model.ops.alloc3f(*Y.shape)
|
|
for t in range(Xp.data.shape[0]):
|
|
masks[t] = valid_transitions[prev_actions]
|
|
# Don't train the out-of-bounds sequences.
|
|
masks[t, Xp.size_at_t[t] :] = 0
|
|
# Valid actions get 0*10e8, invalid get -1*10e8
|
|
Y[t] = Xp.data[t] + ((masks[t] - 1) * 10e8)
|
|
prev_actions = Y[t].argmax(axis=-1)
|
|
|
|
def backprop_biluo(dY: Padded) -> Padded:
|
|
# Masking the gradient seems to do poorly here. But why?
|
|
# dY.data *= masks
|
|
return dY
|
|
|
|
return Padded(Y, Xp.size_at_t, Xp.lengths, Xp.indices), backprop_biluo
|
|
|
|
|
|
def get_num_actions(n_labels: int) -> int:
|
|
# One BEGIN action per label
|
|
# One IN action per label
|
|
# One LAST action per label
|
|
# One UNIT action per label
|
|
# One OUT action
|
|
return n_labels * 2 + 1
|
|
|
|
|
|
def _get_transition_table(
|
|
ops: Ops, n_labels: int, _cache: Dict[int, Floats3d] = {}
|
|
) -> Floats3d:
|
|
n_actions = get_num_actions(n_labels)
|
|
if n_actions in _cache:
|
|
return ops.asarray(_cache[n_actions])
|
|
table = ops.alloc2f(n_actions, n_actions)
|
|
B_start, B_end = (0, n_labels)
|
|
I_start, I_end = (B_end, B_end + n_labels)
|
|
O_action = I_end
|
|
B_range = ops.xp.arange(B_start, B_end)
|
|
I_range = ops.xp.arange(I_start, I_end)
|
|
# B and O are always valid
|
|
table[:, B_start:B_end] = 1
|
|
table[:, O_action] = 1
|
|
# I can only follow a matching B
|
|
table[B_range, I_range] = 1
|
|
|
|
_cache[n_actions] = table
|
|
return table
|