mirror of https://github.com/explosion/spaCy.git
Use ftrl training, to learn compressed model.
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@ -124,7 +124,7 @@ cdef class Parser:
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elif 'features' not in cfg:
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cfg['features'] = self.feature_templates
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self.model = ParserModel(cfg['features'])
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self.model.l1_penalty = 1e-7
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self.model.l1_penalty = cfg.get('L1', 0.0)
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self.cfg = cfg
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@ -263,10 +263,10 @@ cdef class Parser:
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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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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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best = arg_max_if_gold(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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self.model.update_weight_ftrl(feat.key, best, -feat.value * eg.c.costs[guess])
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self.model.update_weight_ftrl(feat.key, guess, feat.value * eg.c.costs[guess])
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action = self.moves.c[guess]
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action.do(stcls.c, action.label)
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@ -392,6 +392,14 @@ class ParserStateError(ValueError):
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"Please include the text that the parser failed on, which is:\n"
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"%s" % repr(doc.text))
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cdef int arg_max_if_gold(const weight_t* scores, const weight_t* costs, int n) nogil:
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cdef int best = -1
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for i in range(n):
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if costs[i] <= 0:
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if best == -1 or scores[i] > scores[best]:
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best = i
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return best
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cdef int _arg_max_clas(const weight_t* scores, int move, const Transition* actions,
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int nr_class) except -1:
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