rename seq2seq to encoder_decoder

examples/README.md

@@ -10,7 +10,7 @@ similar API between the different models.
 | [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision.                              |
 | [SQuAD](#squad) | Using BERT for question answering, examples with distributed training.                                                                                  |
 | [Multiple Choice](#multiple choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. 
-| [Seq2seq Model fine-tuning](#seq2seq-model-fine-tuning) | Fine-tuning the library models for seq2seq tasks on the CNN/Daily Mail dataset. |
+| [Abstractive summarization](#abstractive-summarization) | Fine-tuning the library models for abstractive summarization tasks on the CNN/Daily Mail dataset. |
 
 ## Language model fine-tuning
 
@@ -391,7 +391,7 @@ exact_match = 86.91
 This fine-tuned model is available as a checkpoint under the reference
 `bert-large-uncased-whole-word-masking-finetuned-squad`.
 
-## Seq2seq model fine-tuning
+## Abstractive summarization
 
 Based on the script
 [`run_summarization_finetuning.py`](https://github.com/huggingface/transformers/blob/master/examples/run_summarization_finetuning.py).
@@ -408,8 +408,6 @@ note that the finetuning script **will not work** if you do not download both
 datasets. We will refer as `$DATA_PATH` the path to where you uncompressed both
 archive.
 
-## Bert2Bert and abstractive summarization
-
 ```bash
 export DATA_PATH=/path/to/dataset/
 

examples/run_summarization_finetuning.py

@@ -32,7 +32,7 @@ from transformers import (
     AutoTokenizer,
     BertForMaskedLM,
     BertConfig,
-    PreTrainedSeq2seq,
+    PreTrainedEncoderDecoder,
     Model2Model,
 )
 
@@ -475,7 +475,7 @@ def main():
         for checkpoint in checkpoints:
             encoder_checkpoint = os.path.join(checkpoint, "encoder")
             decoder_checkpoint = os.path.join(checkpoint, "decoder")
-            model = PreTrainedSeq2seq.from_pretrained(
+            model = PreTrainedEncoderDecoder.from_pretrained(
                 encoder_checkpoint, decoder_checkpoint
             )
             model.to(args.device)

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. "