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* Remove unused regularize argument from _ml.Model
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@ -18,7 +18,7 @@ cdef int arg_max(const weight_t* scores, const int n_classes) nogil
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cdef class Model:
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cdef int n_classes
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cdef const weight_t* score(self, atom_t* context, bint regularize) except NULL
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cdef const weight_t* score(self, atom_t* context) except NULL
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cdef int update(self, atom_t* context, class_t guess, class_t gold, int cost) except -1
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@ -33,7 +33,7 @@ cdef class Model:
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if self.model_loc and path.exists(self.model_loc):
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self._model.load(self.model_loc, freq_thresh=0)
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cdef const weight_t* score(self, atom_t* context, bint regularize) except NULL:
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cdef const weight_t* score(self, atom_t* context) except NULL:
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cdef int n_feats
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feats = self._extractor.get_feats(context, &n_feats)
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return self._model.get_scores(feats, n_feats)
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@ -274,7 +274,7 @@ cdef class EnPosTagger:
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for i in range(tokens.length):
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if tokens.data[i].pos == 0:
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fill_context(context, i, tokens.data)
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scores = self.model.score(context, False)
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scores = self.model.score(context)
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guess = arg_max(scores, self.model.n_classes)
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tokens.data[i].tag = self.strings[self.tag_names[guess]]
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self.set_morph(i, &self.tags[guess], tokens.data)
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@ -301,7 +301,7 @@ cdef class EnPosTagger:
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correct = 0
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for i in range(tokens.length):
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fill_context(context, i, tokens.data)
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scores = self.model.score(context, True)
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scores = self.model.score(context)
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guess = arg_max(scores, self.model.n_classes)
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loss = guess != golds[i] if golds[i] != -1 else 0
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self.model.update(context, guess, golds[i], loss)
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