Hi guys,
I tried getting tag probabilities from a NER model instead of single tags. I loaded the ner_rus_bert_torch config from deeppavlov.configs.ner and set config[‘chainer’][‘pipe’][2][‘return_probas’] to True.
I get the following error:
NotImplementedError Traceback (most recent call last)
in
----> 1 ner_model_default([‘Мытищинские проститутки посетили короля Испании с ответным визитом’])
/mnt/data/wx1155560/envs/dp/lib/python3.6/site-packages/deeppavlov/core/common/chainer.py in call(self, *args)
205
206 def call(self, *args):
→ 207 return self._compute(*args, param_names=self.in_x, pipe=self.pipe, targets=self.out_params)
208
209 @staticmethod
/mnt/data/wx1155560/envs/dp/lib/python3.6/site-packages/deeppavlov/core/common/chainer.py in _compute(failed resolving arguments)
228 res = component.call(**dict(zip(in_keys, x)))
229 else:
→ 230 res = component.call(*x)
231 if len(out_params) == 1:
232 mem[out_params[0]] = res
/mnt/data/wx1155560/envs/dp/lib/python3.6/site-packages/deeppavlov/core/data/simple_vocab.py in call(self, batch, is_top, **kwargs)
90 def call(self, batch, is_top=True, **kwargs):
91 if isinstance(batch, Iterable) and not isinstance(batch, str):
—> 92 looked_up_batch = [self(sample, is_top=False) for sample in batch]
93 else:
94 return self[batch]
/mnt/data/wx1155560/envs/dp/lib/python3.6/site-packages/deeppavlov/core/data/simple_vocab.py in (.0)
90 def call(self, batch, is_top=True, **kwargs):
91 if isinstance(batch, Iterable) and not isinstance(batch, str):
—> 92 looked_up_batch = [self(sample, is_top=False) for sample in batch]
93 else:
94 return self[batch]
/mnt/data/wx1155560/envs/dp/lib/python3.6/site-packages/deeppavlov/core/data/simple_vocab.py in call(self, batch, is_top, **kwargs)
90 def call(self, batch, is_top=True, **kwargs):
91 if isinstance(batch, Iterable) and not isinstance(batch, str):
—> 92 looked_up_batch = [self(sample, is_top=False) for sample in batch]
93 else:
94 return self[batch]
/mnt/data/wx1155560/envs/dp/lib/python3.6/site-packages/deeppavlov/core/data/simple_vocab.py in (.0)
90 def call(self, batch, is_top=True, **kwargs):
91 if isinstance(batch, Iterable) and not isinstance(batch, str):
—> 92 looked_up_batch = [self(sample, is_top=False) for sample in batch]
93 else:
94 return self[batch]
/mnt/data/wx1155560/envs/dp/lib/python3.6/site-packages/deeppavlov/core/data/simple_vocab.py in call(self, batch, is_top, **kwargs)
90 def call(self, batch, is_top=True, **kwargs):
91 if isinstance(batch, Iterable) and not isinstance(batch, str):
—> 92 looked_up_batch = [self(sample, is_top=False) for sample in batch]
93 else:
94 return self[batch]
/mnt/data/wx1155560/envs/dp/lib/python3.6/site-packages/deeppavlov/core/data/simple_vocab.py in (.0)
90 def call(self, batch, is_top=True, **kwargs):
91 if isinstance(batch, Iterable) and not isinstance(batch, str):
—> 92 looked_up_batch = [self(sample, is_top=False) for sample in batch]
93 else:
94 return self[batch]
/mnt/data/wx1155560/envs/dp/lib/python3.6/site-packages/deeppavlov/core/data/simple_vocab.py in call(self, batch, is_top, **kwargs)
92 looked_up_batch = [self(sample, is_top=False) for sample in batch]
93 else:
—> 94 return self[batch]
95 if self._pad_with_zeros and is_top and not is_str_batch(looked_up_batch):
96 looked_up_batch = zero_pad(looked_up_batch)
/mnt/data/wx1155560/envs/dp/lib/python3.6/site-packages/deeppavlov/core/data/simple_vocab.py in getitem(self, key)
159 return self._t2i[key]
160 else:
→ 161 raise NotImplementedError(“not implemented for type {}”.format(type(key)))
162
163 def contains(self, item):
NotImplementedError: not implemented for type <class 'numpy.float64'>
Looks like you are trying to index a list (list of probs?) with floats.
Is there any way to make the NER model return probabilities?