mirror of https://github.com/explosion/spaCy.git
Use FTRL training in parser
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parent
d108534dc2
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@ -124,6 +124,8 @@ cdef class Parser:
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elif 'features' not in cfg:
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elif 'features' not in cfg:
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cfg['features'] = self.feature_templates
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cfg['features'] = self.feature_templates
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self.model = ParserModel(cfg['features'])
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self.model = ParserModel(cfg['features'])
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self.model.l1_penalty = 1e-7
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self.cfg = cfg
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self.cfg = cfg
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def __reduce__(self):
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def __reduce__(self):
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@ -258,15 +260,20 @@ cdef class Parser:
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self.model.set_featuresC(&eg.c, stcls.c)
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self.model.set_featuresC(&eg.c, stcls.c)
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self.moves.set_costs(eg.c.is_valid, eg.c.costs, stcls, gold)
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self.moves.set_costs(eg.c.is_valid, eg.c.costs, stcls, gold)
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self.model.set_scoresC(eg.c.scores, eg.c.features, eg.c.nr_feat)
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self.model.set_scoresC(eg.c.scores, eg.c.features, eg.c.nr_feat)
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self.model.updateC(&eg.c)
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self.model.time += 1
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guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
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guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
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if eg.c.costs[guess] > 0:
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best = VecVec.arg_max_if_zero(eg.c.scores, eg.c.costs, eg.c.nr_class)
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for feat in eg.c.features[:eg.c.nr_feat]:
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self.model.update_weight_ftrl(feat.key, best, -feat.value * eg.costs[guess])
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self.model.update_weight_ftrl(feat.key, guess, feat.value * eg.costs[guess])
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action = self.moves.c[eg.guess]
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action = self.moves.c[guess]
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action.do(stcls.c, action.label)
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action.do(stcls.c, action.label)
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loss += eg.costs[eg.guess]
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loss += eg.costs[guess]
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eg.fill_scores(0, eg.nr_class)
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eg.fill_scores(0, eg.c.nr_class)
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eg.fill_costs(0, eg.nr_class)
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eg.fill_costs(0, eg.c.nr_class)
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eg.fill_is_valid(1, eg.nr_class)
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eg.fill_is_valid(1, eg.c.nr_class)
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return loss
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return loss
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def step_through(self, Doc doc):
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def step_through(self, Doc doc):
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