Andrei Glinskii · e5b51b56 · e5b51b56 · e5b51b56 · e5b51b56 · e5b51b56
--- a/deeppavlov/dataset_readers/ubuntu_v2_mt_reader.py
+++ b/deeppavlov/dataset_readers/ubuntu_v2_mt_reader.py
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from deeppavlov.core.data.dataset_reader import DatasetReader
+from typing import List, Tuple, Union, Dict
+
+from pathlib import Path
+from deeppavlov.core.common.registry import register
+import csv
--- a/deeppavlov/dataset_readers/ubuntu_v2_reader.py
+++ b/deeppavlov/dataset_readers/ubuntu_v2_reader.py
+from typing import List, Dict, Tuple, Union
+from pathlib import Path
+import csv
+
+from deeppavlov.core.data.dataset_reader import DatasetReader
+from deeppavlov.core.common.registry import register
+from deeppavlov.core.commands.utils import expand_path
+
+@register('ubuntu_v2_reader')
+class UbuntuV2Reader(DatasetReader):
+   """The class to read the Ubuntu V2 dataset from csv files.
+
+   Please, see https://github.com/rkadlec/ubuntu-ranking-dataset-creator.
+
+   Args:
+       data_path: A path to a folder with dataset csv files.
--- a/deeppavlov/configs/ranking/ranking_ubuntu_v2_mt_interact.json
+++ b/deeppavlov/configs/ranking/ranking_ubuntu_v2_mt_interact.json
+       "triplet_loss": false,
+       "batch_size": 256,
+       "save_path": "ubuntu_v2_mt_model/model_weights.h5",
+       "load_path": "ubuntu_v2_mt_model/model_weights.h5"
+     },
+     {
+       "in": ["x_proc"],
+       "in_y": ["y"],
+       "out": ["y_predicted"],
+       "name": "siamese_predictor",
+       "model": "#model",
+       "num_context_turns": "#model.num_context_turns",
+       "batch_size": "#model.batch_size",
+       "interact_pred_num": 3,
+       "responses": "#siam_sent_vocab",
+       "preproc_func": "#preproc.__call__"
--- a/deeppavlov/models/ranking/siamese_model.py
+++ b/deeppavlov/models/ranking/siamese_model.py
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+from typing import List, Iterable, Union
+
+from deeppavlov.core.models.nn_model import NNModel
+
+
+class SiameseModel(NNModel):
+   """The class implementing base functionality for siamese neural networks.
+
+   Args:
+       batch_size: A size of a batch.
--- a/deeppavlov/models/ranking/siamese_model.py
+++ b/deeppavlov/models/ranking/siamese_model.py
+# Copyright 2017 Neural Networks and Deep Learning lab, MIPT
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+from typing import List, Iterable, Union
--- a/deeppavlov/models/ranking/siamese_predictor.py
+++ b/deeppavlov/models/ranking/siamese_predictor.py
+       self.batch_size = batch_size
+       self.num_context_turns = num_context_turns
+       self.ranking = ranking
+       self.attention = attention
+       self.responses = {el[1]: el[0] for el in responses.items()}
+       self.preproc_responses = []
+       self.response_embeddings = None
+       self.preproc_func = preproc_func
+       self.interact_pred_num = interact_pred_num
+       self.model = model
+       self._build_preproc_responses()
+       if not self.attention:
+           self._build_response_embeddings()
+
+   def __call__(self, batch: Iterable[List[np.ndarray]]) -> List[Union[List[str],str]]:
+       context = next(batch)
--- a/deeppavlov/models/embedders/fasttext_embedder.py
+++ b/deeppavlov/models/embedders/fasttext_embedder.py
       self.pad_zero = pad_zero
       self.model = self.load()

+   def destroy(self):
+       del self.model