clean up run_seq2seq.py

convlab2/base_models/t5/run_seq2seq.py

-#!python
+#!/usr/bin/env python
 # coding=utf-8
 # Copyright 2021 The HuggingFace Team. All rights reserved.
 #
@@ -27,12 +27,10 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import datasets
-import nltk  # Here to have a nice missing dependency error message early on
 import numpy as np
-from datasets import load_dataset, load_metric
+from datasets import load_dataset
 
 import transformers
-from filelock import FileLock
 from transformers import (
     AutoConfig,
     AutoModelForSeq2SeqLM,
@@ -43,7 +41,6 @@ from transformers import (
     Seq2SeqTrainingArguments,
     set_seed,
 )
-from transformers.file_utils import is_offline_mode
 from transformers.trainer_utils import EvalPrediction, get_last_checkpoint
 from transformers.utils import check_min_version
 from transformers.utils.versions import require_version
@@ -109,7 +106,7 @@ class DataTrainingArguments:
 
     
     task_name: Optional[str] = field(
-        default=None, metadata={"help": "The name of the task, e.g., response generation."}
+        default=None, metadata={"help": "The name of the task, e.g., rg (for rgresponse generation)."}
     )
     dataset_name: Optional[str] = field(
         default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
@@ -223,7 +220,7 @@ class DataTrainingArguments:
             and self.validation_file is None
             and self.test_file is None
         ):
-            raise ValueError("Need either a dataset name or a training/validation file/test_file.")
+            raise ValueError("Need either a dataset name or a training/validation/testing file.")
         else:
             if self.train_file is not None:
                 extension = self.train_file.split(".")[-1]
@@ -238,8 +235,6 @@ class DataTrainingArguments:
             self.val_max_target_length = self.max_target_length
 
 
-
-
 def main():
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
@@ -586,6 +581,7 @@ def main():
         trainer.log_metrics("eval", metrics)
         trainer.save_metrics("eval", metrics)
 
+    # Predict
     if training_args.do_predict:
         logger.info("*** Predict ***")