add base for seq2seq finetuning

examples/run_seq2seq_finetuning.py

@@ -0,0 +1,67 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018 Microsoft and The HuggingFace Inc.  All rights reserved.
+#
+# 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.
+""" Finetuning seq2seq models for sequence generation.
+
+We use the procedure described in [1] to finetune models for sequence
+generation. Let S1 and S2 be the source and target sequence respectively; we
+pack them using the start of sequence [SOS] and end of sequence [EOS] token:
+
+    [SOS] S1 [EOS] S2 [EOS]
+
+We then mask a fixed percentage of token from S2 at random and learn to predict
+the masked words. [EOS] can be masked during finetuning so the model learns to
+terminate the generation process.
+
+[1] Dong Li, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng
+Gao, Ming Zhou, and Hsiao-Wuen Hon.  “Unified Language Model Pre-Training for
+Natural Language Understanding and Generation.” (May 2019) ArXiv:1905.03197
+"""
+
+import logging
+import random
+
+import numpy as np
+import torch
+
+logger = logging.getLogger(__name__)
+
+
+def set_seed(args):
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    if args.n_gpu > 0:
+        torch.cuda.manual_seed_all(args.seed)
+
+
+def train(args, train_dataset, model, tokenizer):
+    """ Fine-tune the pretrained model on the corpus. """
+    # Data sampler
+    # Data loader
+    # Training
+    raise NotImplementedError
+
+
+def evaluate(args, model, tokenizer, prefix=""):
+    raise NotImplementedError
+
+
+def main():
+    raise NotImplementedError
+
+
+def __main__():
+    main()