rename seq2seq to encoder_decoder

transformers/__init__.py

@@ -87,7 +87,7 @@ if is_torch_available():
     from .modeling_distilbert import (DistilBertForMaskedLM, DistilBertModel,
                                 DistilBertForSequenceClassification, DistilBertForQuestionAnswering,
                                 DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP)
-    from .modeling_seq2seq import PreTrainedSeq2seq, Model2Model
+    from .modeling_encoder_decoder import PreTrainedEncoderDecoder, Model2Model
 
     # Optimization
     from .optimization import (AdamW, ConstantLRSchedule, WarmupConstantSchedule, WarmupCosineSchedule,

transformers/modeling_encoder_decoder.py

@@ -12,7 +12,7 @@
 # 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.
-""" Auto Model class. """
+""" Classes to support Encoder-Decoder architectures """
 
 from __future__ import absolute_import, division, print_function, unicode_literals
 
@@ -27,9 +27,9 @@ from .modeling_auto import AutoModel, AutoModelWithLMHead
 logger = logging.getLogger(__name__)
 
 
-class PreTrainedSeq2seq(nn.Module):
+class PreTrainedEncoderDecoder(nn.Module):
     r"""
-        :class:`~transformers.PreTrainedSeq2seq` is a generic model class that will be
+        :class:`~transformers.PreTrainedEncoderDecoder` is a generic model class that will be
         instantiated as a transformer architecture with one of the base model
         classes of the library as encoder and (optionally) another one as
         decoder when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
@@ -37,7 +37,7 @@ class PreTrainedSeq2seq(nn.Module):
     """
 
     def __init__(self, encoder, decoder):
-        super(PreTrainedSeq2seq, self).__init__()
+        super(PreTrainedEncoderDecoder, self).__init__()
         self.encoder = encoder
         self.decoder = decoder
 
@@ -107,7 +107,7 @@ class PreTrainedSeq2seq(nn.Module):
 
         Examples::
 
-            model = PreTrainedSeq2seq.from_pretained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert
+            model = PreTrainedEncoderDecoder.from_pretained('bert-base-uncased', 'bert-base-uncased') # initialize Bert2Bert
         """
 
         # keyword arguments come in 3 flavors: encoder-specific (prefixed by
@@ -155,7 +155,7 @@ class PreTrainedSeq2seq(nn.Module):
 
     def save_pretrained(self, save_directory):
         """ Save a Seq2Seq model and its configuration file in a format such
-        that it can be loaded using `:func:`~transformers.PreTrainedSeq2seq.from_pretrained`
+        that it can be loaded using `:func:`~transformers.PreTrainedEncoderDecoder.from_pretrained`
 
         We save the encoder' and decoder's parameters in two separate directories.
         """
@@ -219,7 +219,7 @@ class PreTrainedSeq2seq(nn.Module):
         return decoder_outputs + encoder_outputs
 
 
-class Model2Model(PreTrainedSeq2seq):
+class Model2Model(PreTrainedEncoderDecoder):
     r"""
         :class:`~transformers.Model2Model` instantiates a Seq2Seq2 model
         where both of the encoder and decoder are of the same family. If the
@@ -277,14 +277,14 @@ class Model2Model(PreTrainedSeq2seq):
         return model
 
 
-class Model2LSTM(PreTrainedSeq2seq):
+class Model2LSTM(PreTrainedEncoderDecoder):
     @classmethod
     def from_pretrained(cls, *args, **kwargs):
         if kwargs.get("decoder_model", None) is None:
             # We will create a randomly initilized LSTM model as decoder
             if "decoder_config" not in kwargs:
                 raise ValueError(
-                    "To load an LSTM in Seq2seq model, please supply either: "
+                    "To load an LSTM in Encoder-Decoder model, please supply either: "
                     "    - a torch.nn.LSTM model as `decoder_model` parameter (`decoder_model=lstm_model`), or"
                     "    - a dictionary of configuration parameters that will be used to initialize a"
                     "      torch.nn.LSTM model as `decoder_config` keyword argument. "