Repository: huggingface/transformers

Issue:
[example scripts] disambiguate language specification API
Currently in example scripts like `run_seq2seq.py` we have:

1. for t5
```
--task translation_en_to_ro
--source_prefix "translate English to Romanian: "
```

2. Also these 2:
```
--target_lang ro_RO
--source_lang en_XX
```
are used only for MBart and are ignored for other models. Which means that people will unknowingly try to use these two as well when they aren't need.
 
The problem in both situations is that we provide error-prone API where a user wants to change the language and forgets that there is more than one of the same and changes only one of the sets of languages, but not the other, which leads to broken outcome.

If such an error is made the specification supplied by the user becomes ambiguous, because one can't tell which of the multiple inputs takes precedence.

Proposal: There should be only one way to input a set of languages and not multiple ways.

Specifically:

- in case 1, probably the easiest is to leave `--task translation_en_to_ro` and auto-generate `--source_prefix "translate English to Romanian: "`
- in case 2, assert if `--target_lang` or `--source_lang` are passed and the model is not MBart.

Thinking more about it, case 1 is a must to solve, because if a user misses `--source_prefix` or makes a typo in it - the train/eval won't fail, but will mysteriously produce really bad outcome. This is not user-friendly.

@sgugger, @patrickvonplaten, @patil-suraj 


Discussion:
Regarding "case 1", only the "old" T5 models: `t5-small`, `t5-base`, `t5-large`, `t5-3b` and `t5-11b` were trained with the `source_prefix` and not the new T5 models. Also IMO, there is a very legitimate case that people might want to fine-tune `t5-small`, `t5-base`, ... on translation, but don't want to condition the model on the prefix. In the second case, I agree more that we should probably raise if `--target_lang` and/or `--source_lang` are not given. For the first case, I'm fine with adding a warning
So if it is one of the 4 models that you listed print a warning to set `--source_prefix` if it's an exact match for model name and the flag wasn't passed, right?

Is it just `run_seq2seq.py` or are there any other scripts that need these 2 special supports?
Yeah, I think this would be a good idea! Think it's only T5 so only `run_seq2seq.py`

Resolving patch:
diff --git a/examples/seq2seq/run_seq2seq.py b/examples/seq2seq/run_summarization.py
similarity index 79%
rename from examples/seq2seq/run_seq2seq.py
rename to examples/seq2seq/run_summarization.py
--- a/examples/seq2seq/run_seq2seq.py
+++ b/examples/seq2seq/run_summarization.py
@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 # coding=utf-8
-# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
+# Copyright 2021 The HuggingFace Team. 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.
@@ -20,7 +20,6 @@
 
 import logging
 import os
-import re
 import sys
 from dataclasses import dataclass, field
 from typing import Optional
@@ -37,8 +36,6 @@
     AutoTokenizer,
     DataCollatorForSeq2Seq,
     HfArgumentParser,
-    MBartTokenizer,
-    MBartTokenizerFast,
     Seq2SeqTrainer,
     Seq2SeqTrainingArguments,
     default_data_collator,
@@ -103,13 +100,6 @@ class DataTrainingArguments:
     Arguments pertaining to what data we are going to input our model for training and eval.
     """
 
-    task: str = field(
-        default="summarization",
-        metadata={
-            "help": "The name of the task, should be summarization (or summarization_{dataset} for evaluating "
-            "pegasus) or translation (or translation_{xx}_to_{yy})."
-        },
-    )
     dataset_name: Optional[str] = field(
         default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
     )
@@ -130,15 +120,14 @@ class DataTrainingArguments:
     validation_file: Optional[str] = field(
         default=None,
         metadata={
-            "help": "An optional input evaluation data file to evaluate the metrics (rouge/sacreblue) on "
+            "help": "An optional input evaluation data file to evaluate the metrics (rouge) on "
             "(a jsonlines or csv file)."
         },
     )
     test_file: Optional[str] = field(
         default=None,
         metadata={
-            "help": "An optional input test data file to evaluate the metrics (rouge/sacreblue) on "
-            "(a jsonlines or csv file)."
+            "help": "An optional input test data file to evaluate the metrics (rouge) on " "(a jsonlines or csv file)."
         },
     )
     overwrite_cache: bool = field(
@@ -200,8 +189,6 @@ class DataTrainingArguments:
             "value if set."
         },
     )
-    source_lang: Optional[str] = field(default=None, metadata={"help": "Source language id for translation."})
-    target_lang: Optional[str] = field(default=None, metadata={"help": "Target language id for translation."})
     num_beams: Optional[int] = field(
         default=None,
         metadata={
@@ -229,10 +216,6 @@ def __post_init__(self):
             if self.validation_file is not None:
                 extension = self.validation_file.split(".")[-1]
                 assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
-        if not self.task.startswith("summarization") and not self.task.startswith("translation"):
-            raise ValueError(
-                "`task` should be summarization, summarization_{dataset}, translation or translation_{xx}_to_{yy}."
-            )
         if self.val_max_target_length is None:
             self.val_max_target_length = self.max_target_length
 
@@ -265,6 +248,18 @@ def main():
     else:
         model_args, data_args, training_args = parser.parse_args_into_dataclasses()
 
+    if data_args.source_prefix is None and model_args.model_name_or_path in [
+        "t5-small",
+        "t5-base",
+        "t5-large",
+        "t5-3b",
+        "t5-11b",
+    ]:
+        logger.warning(
+            "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
+            "`--source_prefix 'summarize: ' `"
+        )
+
     # Detecting last checkpoint.
     last_checkpoint = None
     if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
@@ -305,11 +300,8 @@ def main():
     # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
     # (the dataset will be downloaded automatically from the datasets Hub).
     #
-    # For CSV/JSON files in the summarization task, this script will use the first column for the full texts and the
-    # second column for the summaries (unless you specify column names for this with the `text_column` and
-    # `summary_column` arguments).
-    # For translation, only JSON files are supported, with one field named "translation" containing two keys for the
-    # source and target languages (unless you adapt what follows).
+    # For CSV/JSON files this script will use the first column for the full texts and the second column for the
+    # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
     #
     # In distributed training, the load_dataset function guarantee that only one local process can concurrently
     # download the dataset.
@@ -358,16 +350,6 @@ def main():
         use_auth_token=True if model_args.use_auth_token else None,
     )
 
-    # Set decoder_start_token_id
-    if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
-        assert (
-            data_args.target_lang is not None and data_args.source_lang is not None
-        ), "mBart requires --target_lang and --source_lang"
-        if isinstance(tokenizer, MBartTokenizer):
-            model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.target_lang]
-        else:
-            model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.target_lang)
-
     if model.config.decoder_start_token_id is None:
         raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
 
@@ -385,55 +367,24 @@ def main():
         logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
         return
 
-    # For translation we set the codes of our source and target languages (only useful for mBART, the others will
-    # ignore those attributes).
-    if data_args.task.startswith("translation") or isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
-        if data_args.source_lang is not None:
-            tokenizer.src_lang = data_args.source_lang
-        if data_args.target_lang is not None:
-            tokenizer.tgt_lang = data_args.target_lang
-
-    # To serialize preprocess_function below, each of those four variables needs to be defined (even if we won't use
-    # them all).
-    source_lang, target_lang, text_column, summary_column = None, None, None, None
-
-    if data_args.task.startswith("summarization"):
-        # Get the column names for input/target.
-        dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
-        if data_args.text_column is None:
-            text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
-        else:
-            text_column = data_args.text_column
-            if text_column not in column_names:
-                raise ValueError(
-                    f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
-                )
-        if data_args.summary_column is None:
-            summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
-        else:
-            summary_column = data_args.summary_column
-            if summary_column not in column_names:
-                raise ValueError(
-                    f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
-                )
+    # Get the column names for input/target.
+    dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
+    if data_args.text_column is None:
+        text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
     else:
-        # Get the language codes for input/target.
-        lang_search = re.match("translation_([a-z]+)_to_([a-z]+)", data_args.task)
-        if data_args.source_lang is not None:
-            source_lang = data_args.source_lang.split("_")[0]
-        else:
-            assert (
-                lang_search is not None
-            ), "Provide a source language via --source_lang or rename your task 'translation_xx_to_yy'."
-            source_lang = lang_search.groups()[0]
-
-        if data_args.target_lang is not None:
-            target_lang = data_args.target_lang.split("_")[0]
-        else:
-            assert (
-                lang_search is not None
-            ), "Provide a target language via --target_lang or rename your task 'translation_xx_to_yy'."
-            target_lang = lang_search.groups()[1]
+        text_column = data_args.text_column
+        if text_column not in column_names:
+            raise ValueError(
+                f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
+            )
+    if data_args.summary_column is None:
+        summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
+    else:
+        summary_column = data_args.summary_column
+        if summary_column not in column_names:
+            raise ValueError(
+                f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
+            )
 
     # Temporarily set max_target_length for training.
     max_target_length = data_args.max_target_length
@@ -446,12 +397,8 @@ def main():
         )
 
     def preprocess_function(examples):
-        if data_args.task.startswith("translation"):
-            inputs = [ex[source_lang] for ex in examples["translation"]]
-            targets = [ex[target_lang] for ex in examples["translation"]]
-        else:
-            inputs = examples[text_column]
-            targets = examples[summary_column]
+        inputs = examples[text_column]
+        targets = examples[summary_column]
         inputs = [prefix + inp for inp in inputs]
         model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)
 
@@ -526,19 +473,15 @@ def preprocess_function(examples):
         )
 
     # Metric
-    metric_name = "rouge" if data_args.task.startswith("summarization") else "sacrebleu"
-    metric = load_metric(metric_name)
+    metric = load_metric("rouge")
 
     def postprocess_text(preds, labels):
         preds = [pred.strip() for pred in preds]
         labels = [label.strip() for label in labels]
 
         # rougeLSum expects newline after each sentence
-        if metric_name == "rouge":
-            preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
-            labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
-        else:  # sacrebleu
-            labels = [[label] for label in labels]
+        preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
+        labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
 
         return preds, labels
 
@@ -555,13 +498,9 @@ def compute_metrics(eval_preds):
         # Some simple post-processing
         decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
 
-        if metric_name == "rouge":
-            result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
-            # Extract a few results from ROUGE
-            result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
-        else:
-            result = metric.compute(predictions=decoded_preds, references=decoded_labels)
-            result = {"bleu": result["score"]}
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
+        # Extract a few results from ROUGE
+        result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
 
         prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
         result["gen_len"] = np.mean(prediction_lens)
@@ -601,6 +540,7 @@ def compute_metrics(eval_preds):
         trainer.save_state()
 
     # Evaluation
+    results = {}
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
 
@@ -613,7 +553,6 @@ def compute_metrics(eval_preds):
         trainer.log_metrics("eval", metrics)
         trainer.save_metrics("eval", metrics)
 
-    # predict
     if training_args.do_predict:
         logger.info("*** Test ***")
 
@@ -640,6 +579,8 @@ def compute_metrics(eval_preds):
                 with open(output_test_preds_file, "w") as writer:
                     writer.write("\n".join(test_preds))
 
+    return results
+
 
 def _mp_fn(index):
     # For xla_spawn (TPUs)
diff --git a/examples/seq2seq/run_translation.py b/examples/seq2seq/run_translation.py
new file mode 100755
--- /dev/null
+++ b/examples/seq2seq/run_translation.py
@@ -0,0 +1,558 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright The HuggingFace Team and The HuggingFace Inc. team. 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.
+"""
+Fine-tuning the library models for sequence to sequence.
+"""
+# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Optional
+
+import numpy as np
+from datasets import load_dataset, load_metric
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForSeq2SeqLM,
+    AutoTokenizer,
+    DataCollatorForSeq2Seq,
+    HfArgumentParser,
+    MBartTokenizer,
+    MBartTokenizerFast,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+            "with private models)."
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    source_lang: str = field(default=None, metadata={"help": "Source language id for translation."})
+    target_lang: str = field(default=None, metadata={"help": "Target language id for translation."})
+
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a jsonlines)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "An optional input evaluation data file to evaluate the metrics (sacreblue) on "
+            "a jsonlines file."
+        },
+    )
+    test_file: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "An optional input test data file to evaluate the metrics (sacreblue) on " "a jsonlines file."
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_source_length: Optional[int] = field(
+        default=1024,
+        metadata={
+            "help": "The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded."
+        },
+    )
+    max_target_length: Optional[int] = field(
+        default=128,
+        metadata={
+            "help": "The maximum total sequence length for target text after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded."
+        },
+    )
+    val_max_target_length: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
+            "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
+            "during ``evaluate`` and ``predict``."
+        },
+    )
+    pad_to_max_length: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to pad all samples to model maximum sentence length. "
+            "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
+            "efficient on GPU but very bad for TPU."
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_val_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
+            "value if set."
+        },
+    )
+    max_test_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of test examples to this "
+            "value if set."
+        },
+    )
+    num_beams: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
+            "which is used during ``evaluate`` and ``predict``."
+        },
+    )
+    ignore_pad_token_for_loss: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
+        },
+    )
+    source_prefix: Optional[str] = field(
+        default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
+    )
+
+    def __post_init__(self):
+        if self.dataset_name is None and self.train_file is None and self.validation_file is None:
+            raise ValueError("Need either a dataset name or a training/validation file.")
+        elif self.source_lang is None or self.target_lang is None:
+            raise ValueError("Need to specify the source language and the target language.")
+
+        if self.train_file is not None:
+            extension = self.train_file.split(".")[-1]
+            assert extension == "json", "`train_file` should be a json file."
+        if self.validation_file is not None:
+            extension = self.validation_file.split(".")[-1]
+            assert extension == "json", "`validation_file` should be a json file."
+        if self.val_max_target_length is None:
+            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.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    if data_args.source_prefix is None and model_args.model_name_or_path in [
+        "t5-small",
+        "t5-base",
+        "t5-large",
+        "t5-3b",
+        "t5-11b",
+    ]:
+        logger.warning(
+            "You're running a t5 model but didn't provide a source prefix, which is expected, e.g. with "
+            "`--source_prefix 'translate English to German: ' `"
+        )
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own JSON training and evaluation files (see below)
+    # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
+    # (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For translation, only JSON files are supported, with one field named "translation" containing two keys for the
+    # source and target languages (unless you adapt what follows).
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
+    else:
+        data_files = {}
+        if data_args.train_file is not None:
+            data_files["train"] = data_args.train_file
+            extension = data_args.train_file.split(".")[-1]
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.validation_file
+            extension = data_args.validation_file.split(".")[-1]
+        if data_args.test_file is not None:
+            data_files["test"] = data_args.test_file
+            extension = data_args.test_file.split(".")[-1]
+        datasets = load_dataset(extension, data_files=data_files)
+    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
+    # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+    # Load pretrained model and tokenizer
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    model = AutoModelForSeq2SeqLM.from_pretrained(
+        model_args.model_name_or_path,
+        from_tf=bool(".ckpt" in model_args.model_name_or_path),
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    # Set decoder_start_token_id
+    if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
+        assert (
+            data_args.target_lang is not None and data_args.source_lang is not None
+        ), "mBart requires --target_lang and --source_lang"
+        if isinstance(tokenizer, MBartTokenizer):
+            model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.target_lang]
+        else:
+            model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.target_lang)
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
+
+    # Preprocessing the datasets.
+    # We need to tokenize inputs and targets.
+    if training_args.do_train:
+        column_names = datasets["train"].column_names
+    elif training_args.do_eval:
+        column_names = datasets["validation"].column_names
+    elif training_args.do_predict:
+        column_names = datasets["test"].column_names
+    else:
+        logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
+        return
+
+    # For translation we set the codes of our source and target languages (only useful for mBART, the others will
+    # ignore those attributes).
+    if isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
+        if data_args.source_lang is not None:
+            tokenizer.src_lang = data_args.source_lang
+        if data_args.target_lang is not None:
+            tokenizer.tgt_lang = data_args.target_lang
+
+    # Get the language codes for input/target.
+    source_lang = data_args.source_lang.split("_")[0]
+    target_lang = data_args.target_lang.split("_")[0]
+
+    # Temporarily set max_target_length for training.
+    max_target_length = data_args.max_target_length
+    padding = "max_length" if data_args.pad_to_max_length else False
+
+    if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
+        logger.warn(
+            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
+            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
+        )
+
+    def preprocess_function(examples):
+        inputs = [ex[source_lang] for ex in examples["translation"]]
+        targets = [ex[target_lang] for ex in examples["translation"]]
+        inputs = [prefix + inp for inp in inputs]
+        model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)
+
+        # Setup the tokenizer for targets
+        with tokenizer.as_target_tokenizer():
+            labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)
+
+        # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
+        # padding in the loss.
+        if padding == "max_length" and data_args.ignore_pad_token_for_loss:
+            labels["input_ids"] = [
+                [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
+            ]
+
+        model_inputs["labels"] = labels["input_ids"]
+        return model_inputs
+
+    if training_args.do_train:
+        train_dataset = datasets["train"]
+        if "train" not in datasets:
+            raise ValueError("--do_train requires a train dataset")
+        if data_args.max_train_samples is not None:
+            train_dataset = train_dataset.select(range(data_args.max_train_samples))
+        train_dataset = train_dataset.map(
+            preprocess_function,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+        )
+
+    if training_args.do_eval:
+        max_target_length = data_args.val_max_target_length
+        if "validation" not in datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = datasets["validation"]
+        if data_args.max_val_samples is not None:
+            eval_dataset = eval_dataset.select(range(data_args.max_val_samples))
+        eval_dataset = eval_dataset.map(
+            preprocess_function,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+        )
+
+    if training_args.do_predict:
+        max_target_length = data_args.val_max_target_length
+        if "test" not in datasets:
+            raise ValueError("--do_predict requires a test dataset")
+        test_dataset = datasets["test"]
+        if data_args.max_test_samples is not None:
+            test_dataset = test_dataset.select(range(data_args.max_test_samples))
+        test_dataset = test_dataset.map(
+            preprocess_function,
+            batched=True,
+            num_proc=data_args.preprocessing_num_workers,
+            remove_columns=column_names,
+            load_from_cache_file=not data_args.overwrite_cache,
+        )
+
+    # Data collator
+    label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
+    if data_args.pad_to_max_length:
+        data_collator = default_data_collator
+    else:
+        data_collator = DataCollatorForSeq2Seq(
+            tokenizer,
+            model=model,
+            label_pad_token_id=label_pad_token_id,
+            pad_to_multiple_of=8 if training_args.fp16 else None,
+        )
+
+    # Metric
+    metric = load_metric("sacrebleu")
+
+    def postprocess_text(preds, labels):
+        preds = [pred.strip() for pred in preds]
+        labels = [[label.strip()] for label in labels]
+
+        return preds, labels
+
+    def compute_metrics(eval_preds):
+        preds, labels = eval_preds
+        if isinstance(preds, tuple):
+            preds = preds[0]
+        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
+        if data_args.ignore_pad_token_for_loss:
+            # Replace -100 in the labels as we can't decode them.
+            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+        # Some simple post-processing
+        decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
+
+        result = metric.compute(predictions=decoded_preds, references=decoded_labels)
+        result = {"bleu": result["score"]}
+
+        prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
+        result["gen_len"] = np.mean(prediction_lens)
+        result = {k: round(v, 4) for k, v in result.items()}
+        return result
+
+    # Initialize our Trainer
+    trainer = Seq2SeqTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
+    )
+
+    # Training
+    if training_args.do_train:
+        if last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        elif os.path.isdir(model_args.model_name_or_path):
+            checkpoint = model_args.model_name_or_path
+        else:
+            checkpoint = None
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        metrics = trainer.evaluate(
+            max_length=data_args.val_max_target_length, num_beams=data_args.num_beams, metric_key_prefix="eval"
+        )
+        max_val_samples = data_args.max_val_samples if data_args.max_val_samples is not None else len(eval_dataset)
+        metrics["eval_samples"] = min(max_val_samples, len(eval_dataset))
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    if training_args.do_predict:
+        logger.info("*** Test ***")
+
+        test_results = trainer.predict(
+            test_dataset,
+            metric_key_prefix="test",
+            max_length=data_args.val_max_target_length,
+            num_beams=data_args.num_beams,
+        )
+        metrics = test_results.metrics
+        max_test_samples = data_args.max_test_samples if data_args.max_test_samples is not None else len(test_dataset)
+        metrics["test_samples"] = min(max_test_samples, len(test_dataset))
+
+        trainer.log_metrics("test", metrics)
+        trainer.save_metrics("test", metrics)
+
+        if trainer.is_world_process_zero():
+            if training_args.predict_with_generate:
+                test_preds = tokenizer.batch_decode(
+                    test_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
+                )
+                test_preds = [pred.strip() for pred in test_preds]
+                output_test_preds_file = os.path.join(training_args.output_dir, "test_generations.txt")
+                with open(output_test_preds_file, "w") as writer:
+                    writer.write("\n".join(test_preds))
+
+    return results
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()


Test patch:
diff --git a/examples/test_examples.py b/examples/test_examples.py
--- a/examples/test_examples.py
+++ b/examples/test_examples.py
@@ -49,8 +49,9 @@
     import run_mlm
     import run_ner
     import run_qa as run_squad
-    import run_seq2seq
+    import run_summarization
     import run_swag
+    import run_translation
 
 
 logging.basicConfig(level=logging.DEBUG)
@@ -277,15 +278,14 @@ def test_generation(self):
             self.assertGreaterEqual(len(result[0]), 10)
 
     @slow
-    def test_run_seq2seq_summarization(self):
+    def test_run_summarization(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
 
         tmp_dir = self.get_auto_remove_tmp_dir()
         testargs = f"""
-            run_seq2seq.py
+            run_summarization.py
             --model_name_or_path t5-small
-            --task summarization
             --train_file tests/fixtures/tests_samples/xsum/sample.json
             --validation_file tests/fixtures/tests_samples/xsum/sample.json
             --output_dir {tmp_dir}
@@ -301,7 +301,7 @@ def test_run_seq2seq_summarization(self):
         """.split()
 
         with patch.object(sys, "argv", testargs):
-            run_seq2seq.main()
+            run_summarization.main()
             result = get_results(tmp_dir)
             self.assertGreaterEqual(result["eval_rouge1"], 10)
             self.assertGreaterEqual(result["eval_rouge2"], 2)
@@ -309,15 +309,16 @@ def test_run_seq2seq_summarization(self):
             self.assertGreaterEqual(result["eval_rougeLsum"], 7)
 
     @slow
-    def test_run_seq2seq_translation(self):
+    def test_run_translation(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
 
         tmp_dir = self.get_auto_remove_tmp_dir()
         testargs = f"""
-            run_seq2seq.py
+            run_translation.py
             --model_name_or_path sshleifer/student_marian_en_ro_6_1
-            --task translation_en_to_ro
+            --source_lang en
+            --target_lang ro
             --train_file tests/fixtures/tests_samples/wmt16/sample.json
             --validation_file tests/fixtures/tests_samples/wmt16/sample.json
             --output_dir {tmp_dir}
@@ -335,6 +336,6 @@ def test_run_seq2seq_translation(self):
         """.split()
 
         with patch.object(sys, "argv", testargs):
-            run_seq2seq.main()
+            run_translation.main()
             result = get_results(tmp_dir)
             self.assertGreaterEqual(result["eval_bleu"], 30)
diff --git a/examples/tests/deepspeed/test_deepspeed.py b/examples/tests/deepspeed/test_deepspeed.py
--- a/examples/tests/deepspeed/test_deepspeed.py
+++ b/examples/tests/deepspeed/test_deepspeed.py
@@ -233,7 +233,6 @@ def run_trainer(
             --group_by_length
             --label_smoothing_factor 0.1
             --adafactor
-            --task translation
             --target_lang ro_RO
             --source_lang en_XX
         """.split()
@@ -246,7 +245,7 @@ def run_trainer(
             args = [x for x in args if x not in remove_args]
 
         ds_args = f"--deepspeed {self.test_file_dir_str}/ds_config.json".split()
-        script = [f"{self.examples_dir_str}/seq2seq/run_seq2seq.py"]
+        script = [f"{self.examples_dir_str}/seq2seq/run_translation.py"]
         num_gpus = get_gpu_count() if distributed else 1
         launcher = f"deepspeed --num_gpus {num_gpus}".split()
 
diff --git a/examples/tests/trainer/test_trainer_ext.py b/examples/tests/trainer/test_trainer_ext.py
--- a/examples/tests/trainer/test_trainer_ext.py
+++ b/examples/tests/trainer/test_trainer_ext.py
@@ -35,7 +35,7 @@
 
 bindir = os.path.abspath(os.path.dirname(__file__))
 sys.path.append(f"{bindir}/../../seq2seq")
-from run_seq2seq import main  # noqa
+from run_translation import main  # noqa
 
 
 set_seed(42)
@@ -209,7 +209,6 @@ def run_trainer(
             --group_by_length
             --label_smoothing_factor 0.1
             --adafactor
-            --task translation
             --target_lang ro_RO
             --source_lang en_XX
         """
@@ -226,12 +225,12 @@ def run_trainer(
             distributed_args = f"""
                 -m torch.distributed.launch
                 --nproc_per_node={n_gpu}
-                {self.examples_dir_str}/seq2seq/run_seq2seq.py
+                {self.examples_dir_str}/seq2seq/run_translation.py
             """.split()
             cmd = [sys.executable] + distributed_args + args
             execute_subprocess_async(cmd, env=self.get_env())
         else:
-            testargs = ["run_seq2seq.py"] + args
+            testargs = ["run_translation.py"] + args
             with patch.object(sys, "argv", testargs):
                 main()