Add a template for examples and apply it for mlm and plm examples (#8153)

* Add a template for example scripts and apply it to mlm

* Formatting

* Fix test

* Add plm script

* Add a template for example scripts and apply it to mlm

* Formatting

* Fix test

* Add plm script

* Add a template for example scripts and apply it to mlm

* Formatting

* Fix test

* Add plm script

* Styling

.circleci/config.yml

@@ -320,9 +320,9 @@ jobs:
                   key: v0.4-code_quality-{{ checksum "setup.py" }}
                   paths:
                       - '~/.cache/pip'
-            - run: black --check examples templates tests src utils
-            - run: isort --check-only examples templates tests src utils
-            - run: flake8 examples templates tests src utils
+            - run: black --check examples tests src utils
+            - run: isort --check-only examples tests src utils
+            - run: flake8 examples tests src utils
             - run: python utils/style_doc.py src/transformers docs/source --max_len 119 --check_only
             - run: python utils/check_copies.py
             - run: python utils/check_dummies.py

Makefile

 .PHONY: modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
 
 
-check_dirs := examples templates tests src utils
+check_dirs := examples tests src utils
 
 # get modified files since the branch was made
 fork_point_sha := $(shell git merge-base --fork-point master)

examples/language-modeling/run_clm.py

@@ -18,7 +18,7 @@ Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, .
 Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
 https://huggingface.co/models?filter=causal-lm
 """
-# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
+# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
 
 import logging
 import math
@@ -325,7 +325,7 @@ def main():
         perplexity = math.exp(eval_output["eval_loss"])
         results["perplexity"] = perplexity
 
-        output_eval_file = os.path.join(training_args.output_dir, "eval_results_lm.txt")
+        output_eval_file = os.path.join(training_args.output_dir, "eval_results_clm.txt")
         if trainer.is_world_process_zero():
             with open(output_eval_file, "w") as writer:
                 logger.info("***** Eval results *****")

examples/language-modeling/run_mlm.py

+# coding=utf-8
+# Copyright 2020 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.
+# 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 masked language modeling (BERT, ALBERT, RoBERTa...) on a text file or a dataset.
+
+Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
+https://huggingface.co/models?filter=masked-lm
+"""
+# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
+
+import logging
+import math
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Optional
+
+from datasets import load_dataset
+
+import transformers
+from transformers import (
+    CONFIG_MAPPING,
+    MODEL_FOR_MASKED_LM_MAPPING,
+    AutoConfig,
+    AutoModelForMaskedLM,
+    AutoTokenizer,
+    DataCollatorForLanguageModeling,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    set_seed,
+)
+from transformers.trainer_utils import is_main_process
+
+
+logger = logging.getLogger(__name__)
+MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The model checkpoint for weights initialization."
+            "Don't set if you want to train a model from scratch."
+        },
+    )
+    model_type: Optional[str] = field(
+        default=None,
+        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
+    )
+    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 do you want to store the pretrained models downloaded from s3"}
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    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 text file)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    max_seq_length: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated."
+        },
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    mlm_probability: float = field(
+        default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"}
+    )
+
+    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.")
+        else:
+            if self.train_file is not None:
+                extension = self.train_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
+
+
+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, TrainingArguments))
+    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 (
+        os.path.exists(training_args.output_dir)
+        and os.listdir(training_args.output_dir)
+        and training_args.do_train
+        and not training_args.overwrite_output_dir
+    ):
+        raise ValueError(
+            f"Output directory ({training_args.output_dir}) already exists and is not empty."
+            "Use --overwrite_output_dir to overcome."
+        )
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=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 CSV/JSON/TXT 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 CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this
+    # behavior (see below)
+    #
+    # 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
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.train_file
+        extension = data_args.train_file.split(".")[-1]
+        if extension == "txt":
+            extension = "text"
+        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.
+    if model_args.config_name:
+        config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
+    elif model_args.model_name_or_path:
+        config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
+    else:
+        config = CONFIG_MAPPING[model_args.model_type]()
+        logger.warning("You are instantiating a new config instance from scratch.")
+
+    if model_args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
+        )
+    elif model_args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
+        )
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+
+    if model_args.model_name_or_path:
+        model = AutoModelForMaskedLM.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,
+        )
+    else:
+        logger.info("Training new model from scratch")
+        model = AutoModelForMaskedLM.from_config(config)
+
+    model.resize_token_embeddings(len(tokenizer))
+
+    # Preprocessing the datasets.
+    # First we tokenize all the texts.
+    if training_args.do_train:
+        column_names = datasets["train"].column_names
+    else:
+        column_names = datasets["validation"].column_names
+    text_column_name = "text" if "text" in column_names else column_names[0]
+
+    def tokenize_function(examples):
+        # Remove empty lines
+        examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
+        return tokenizer(examples["text"], truncation=True, max_length=data_args.max_seq_length)
+
+    tokenized_datasets = datasets.map(
+        tokenize_function,
+        batched=True,
+        num_proc=data_args.preprocessing_num_workers,
+        remove_columns=[text_column_name],
+        load_from_cache_file=not data_args.overwrite_cache,
+    )
+
+    # Data collator
+    # This one will take care of randomly masking the tokens.
+    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability)
+
+    # Initialize our Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+    )
+
+    # Training
+    if training_args.do_train:
+        trainer.train(
+            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
+        )
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+    # Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        eval_output = trainer.evaluate()
+
+        perplexity = math.exp(eval_output["eval_loss"])
+        results["perplexity"] = perplexity
+
+        output_eval_file = os.path.join(training_args.output_dir, "eval_results_mlm.txt")
+        if trainer.is_world_process_zero():
+            with open(output_eval_file, "w") as writer:
+                logger.info("***** Eval results *****")
+                for key, value in results.items():
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+    return results
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()

examples/language-modeling/run_plm.py

+# coding=utf-8
+# Copyright 2020 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.
+# 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 permutation language modeling.
+"""
+# You can also adapt this script on your own permutation language modeling task. Pointers for this are left as comments.
+
+import logging
+import math
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Optional
+
+from datasets import load_dataset
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    DataCollatorForPermutationLanguageModeling,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    XLNetConfig,
+    XLNetLMHeadModel,
+    set_seed,
+)
+from transformers.trainer_utils import is_main_process
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The model checkpoint for weights initialization."
+            "Don't set if you want to train a model from scratch."
+        },
+    )
+    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 do you want to store the pretrained models downloaded from s3"}
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    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 text file)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    max_seq_length: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated."
+        },
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    plm_probability: float = field(
+        default=1 / 6,
+        metadata={
+            "help": "Ratio of length of a span of masked tokens to surrounding context length for "
+            "permutation language modeling."
+        },
+    )
+    max_span_length: int = field(
+        default=5, metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."}
+    )
+
+    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.")
+        else:
+            if self.train_file is not None:
+                extension = self.train_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
+
+
+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, TrainingArguments))
+    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 (
+        os.path.exists(training_args.output_dir)
+        and os.listdir(training_args.output_dir)
+        and training_args.do_train
+        and not training_args.overwrite_output_dir
+    ):
+        raise ValueError(
+            f"Output directory ({training_args.output_dir}) already exists and is not empty."
+            "Use --overwrite_output_dir to overcome."
+        )
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=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 CSV/JSON/TXT 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 CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this
+    # behavior (see below)
+    #
+    # 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
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.train_file
+        extension = data_args.train_file.split(".")[-1]
+        if extension == "txt":
+            extension = "text"
+        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.
+    if model_args.config_name:
+        config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
+    elif model_args.model_name_or_path:
+        config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
+    else:
+        config = XLNetConfig()
+        logger.warning("You are instantiating a new config instance from scratch.")
+
+    if model_args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
+        )
+    elif model_args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
+        )
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+
+    if model_args.model_name_or_path:
+        model = XLNetLMHeadModel.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,
+        )
+    else:
+        logger.info("Training new model from scratch")
+        model = XLNetLMHeadModel.from_config(config)
+
+    model.resize_token_embeddings(len(tokenizer))
+
+    # Preprocessing the datasets.
+    # First we tokenize all the texts.
+    if training_args.do_train:
+        column_names = datasets["train"].column_names
+    else:
+        column_names = datasets["validation"].column_names
+    text_column_name = "text" if "text" in column_names else column_names[0]
+
+    def tokenize_function(examples):
+        # Remove empty lines
+        examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()]
+        return tokenizer(examples["text"], truncation=True, max_length=data_args.max_seq_length)
+
+    tokenized_datasets = datasets.map(
+        tokenize_function,
+        batched=True,
+        num_proc=data_args.preprocessing_num_workers,
+        remove_columns=[text_column_name],
+        load_from_cache_file=not data_args.overwrite_cache,
+    )
+
+    # Data collator
+    data_collator = DataCollatorForPermutationLanguageModeling(
+        tokenizer=tokenizer,
+        plm_probability=data_args.plm_probability,
+        max_span_length=data_args.max_span_length,
+    )
+
+    # Initialize our Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+    )
+
+    # Training
+    if training_args.do_train:
+        trainer.train(
+            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
+        )
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+    # Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        eval_output = trainer.evaluate()
+
+        perplexity = math.exp(eval_output["eval_loss"])
+        results["perplexity"] = perplexity
+
+        output_eval_file = os.path.join(training_args.output_dir, "eval_results_plm.txt")
+        if trainer.is_world_process_zero():
+            with open(output_eval_file, "w") as writer:
+                logger.info("***** Eval results *****")
+                for key, value in results.items():
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+    return results
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()

examples/test_examples.py

@@ -37,7 +37,7 @@ if SRC_DIRS is not None:
     import run_clm
     import run_generation
     import run_glue
-    import run_language_modeling
+    import run_mlm
     import run_pl_glue
     import run_squad
 
@@ -160,23 +160,21 @@ class ExamplesTests(TestCasePlus):
             result = run_clm.main()
             self.assertLess(result["perplexity"], 100)
 
-    def test_run_language_modeling(self):
+    def test_run_mlm(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
 
         tmp_dir = self.get_auto_remove_tmp_dir()
         testargs = f"""
-            run_language_modeling.py
+            run_mlm.py
             --model_name_or_path distilroberta-base
-            --model_type roberta
-            --mlm
-            --line_by_line
-            --train_data_file ./tests/fixtures/sample_text.txt
-            --eval_data_file ./tests/fixtures/sample_text.txt
+            --train_file ./tests/fixtures/sample_text.txt
+            --validation_file ./tests/fixtures/sample_text.txt
             --output_dir {tmp_dir}
             --overwrite_output_dir
             --do_train
             --do_eval
+            --prediction_loss_only
             --num_train_epochs=1
         """.split()
 
@@ -184,7 +182,7 @@ class ExamplesTests(TestCasePlus):
             testargs.append("--no_cuda")
 
         with patch.object(sys, "argv", testargs):
-            result = run_language_modeling.main()
+            result = run_mlm.main()
             self.assertLess(result["perplexity"], 42)
 
     def test_run_squad(self):

src/transformers/data/data_collator.py

@@ -520,7 +520,7 @@ class DataCollatorForPermutationLanguageModeling:
                 perm_index.reshape((labels.size(1), 1)) <= perm_index.reshape((1, labels.size(1)))
             ) & masked_indices[i]
 
-        return inputs, perm_mask, target_mapping, labels
+        return inputs.long(), perm_mask, target_mapping, labels.long()
 
 
 @dataclass

templates/adding_a_new_example_script/README.md

-# How to add a new example script in 🤗Transformers
+# How to add a new example script in 🤗 Transformers
 
-This folder provide a template for adding a new example script implementing a training or inference task with the models in the  🤗Transformers library.
-Add tests!
+This folder provide a template for adding a new example script implementing a training or inference task with the
+models in the 🤗 Transformers library. To use it, you will need to install cookiecutter:
+```
+pip install cookiecutter
+```
+or refer to the installation page of the [cookiecutter documentation](https://cookiecutter.readthedocs.io/).
 
+You can then run the following command inside the `examples` folder of the transformers repo:
+```
+cookiecutter ../templates/adding_a_new_example_script/
+```
+and answer the questions asked, which will generate a new folder where you will find a pre-filled template for your
+example following the best practices we recommend for them.
 
-These folder can be put in a subdirectory under your example's name, like `examples/deebert`.
+Adjust the way the data is preprocessed, the model is loaded or the Trainer is instantiated then when you're happy, add
+a `README.md` in the folder (or complete the existing one if you added a script to an existing folder) telling a user
+how to run your script.
 
-
-Best Practices: 
-- use `Trainer`/`TFTrainer`
-- write an @slow test that checks that your model can train on one batch and get a low loss.
-    - this test should use cuda if it's available. (e.g. by checking `transformers.torch_device`)
-- adding an `eval_xxx.py` script that can evaluate a pretrained checkpoint.  
-- tweet about your new example with a carbon screenshot of how to run it and tag @huggingface
+Make a PR to the 🤗 Transformers repo. Don't forget to tweet about your new example with a carbon screenshot of how to
+run it and tag @huggingface!

templates/adding_a_new_example_script/cookiecutter.json

+{
+  "example_name": "text classification",
+  "directory_name": "{{cookiecutter.example_name|lower|replace(' ', '-')}}",
+  "example_shortcut": "{{cookiecutter.directory_name}}",
+  "model_class": "AutoModel",
+  "authors": "The HuggingFace Team",
+  "can_train_from_scratch": ["True", "False"]
+}
\ No newline at end of file

templates/adding_a_new_example_script/run_xxx.py

-# coding=utf-8
-# Copyright 2018 XXX.  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 the library models for task XXX."""
-
-
-import argparse
-import glob
-import logging
-import os
-import random
-
-import numpy as np
-import torch
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
-from torch.utils.data.distributed import DistributedSampler
-from tqdm import tqdm, trange
-
-from transformers import (
-    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
-    WEIGHTS_NAME,
-    AdamW,
-    AutoConfig,
-    AutoModelForQuestionAnswering,
-    AutoTokenizer,
-    get_linear_schedule_with_warmup,
-)
-from utils_squad import (
-    RawResult,
-    RawResultExtended,
-    convert_examples_to_features,
-    read_squad_examples,
-    write_predictions,
-    write_predictions_extended,
-)
-
-# The following import is the official SQuAD evaluation script (2.0).
-# You can remove it from the dependencies if you are using this script outside of the library
-# We've added it here for automated tests (see examples/test_examples.py file)
-from utils_squad_evaluate import EVAL_OPTS
-from utils_squad_evaluate import main as evaluate_on_squad
-
-
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except ImportError:
-    from tensorboardX import SummaryWriter
-
-
-logger = logging.getLogger(__name__)
-
-MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
-MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
-
-
-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 to_list(tensor):
-    return tensor.detach().cpu().tolist()
-
-
-def train(args, train_dataset, model, tokenizer):
-    """ Train the model """
-    if args.local_rank in [-1, 0]:
-        tb_writer = SummaryWriter()
-
-    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
-    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
-    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
-
-    if args.max_steps > 0:
-        t_total = args.max_steps
-        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
-    else:
-        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
-
-    # Prepare optimizer and schedule (linear warmup and decay)
-    no_decay = ["bias", "LayerNorm.weight"]
-    optimizer_grouped_parameters = [
-        {
-            "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
-            "weight_decay": args.weight_decay,
-        },
-        {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
-    ]
-    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
-    scheduler = get_linear_schedule_with_warmup(
-        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
-    )
-    if args.fp16:
-        try:
-            from apex import amp
-        except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
-
-    # multi-gpu training (should be after apex fp16 initialization)
-    if args.n_gpu > 1:
-        model = torch.nn.DataParallel(model)
-
-    # Distributed training (should be after apex fp16 initialization)
-    if args.local_rank != -1:
-        model = torch.nn.parallel.DistributedDataParallel(
-            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
-        )
-
-    # Train!
-    logger.info("***** Running training *****")
-    logger.info("  Num examples = %d", len(train_dataset))
-    logger.info("  Num Epochs = %d", args.num_train_epochs)
-    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
-    logger.info(
-        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
-        args.train_batch_size
-        * args.gradient_accumulation_steps
-        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
-    )
-    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
-    logger.info("  Total optimization steps = %d", t_total)
-
-    global_step = 0
-    tr_loss, logging_loss = 0.0, 0.0
-    model.zero_grad()
-    train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
-    set_seed(args)  # Added here for reproductibility
-    for _ in train_iterator:
-        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
-        for step, batch in enumerate(epoch_iterator):
-            model.train()
-            batch = tuple(t.to(args.device) for t in batch)
-            inputs = {
-                "input_ids": batch[0],
-                "attention_mask": batch[1],
-                "start_positions": batch[3],
-                "end_positions": batch[4],
-            }
-            if args.model_type != "distilbert":
-                inputs["token_type_ids"] = None if args.model_type == "xlm" else batch[2]
-            if args.model_type in ["xlnet", "xlm"]:
-                inputs.update({"cls_index": batch[5], "p_mask": batch[6]})
-            outputs = model(**inputs)
-            loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
-
-            if args.n_gpu > 1:
-                loss = loss.mean()  # mean() to average on multi-gpu parallel (not distributed) training
-            if args.gradient_accumulation_steps > 1:
-                loss = loss / args.gradient_accumulation_steps
-
-            if args.fp16:
-                with amp.scale_loss(loss, optimizer) as scaled_loss:
-                    scaled_loss.backward()
-            else:
-                loss.backward()
-
-            tr_loss += loss.item()
-            if (step + 1) % args.gradient_accumulation_steps == 0:
-                if args.fp16:
-                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
-                else:
-                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
-
-                optimizer.step()
-                scheduler.step()  # Update learning rate schedule
-                model.zero_grad()
-                global_step += 1
-
-                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
-                    # Log metrics
-                    if (
-                        args.local_rank == -1 and args.evaluate_during_training
-                    ):  # Only evaluate when single GPU otherwise metrics may not average well
-                        results = evaluate(args, model, tokenizer)
-                        for key, value in results.items():
-                            tb_writer.add_scalar("eval_{}".format(key), value, global_step)
-                    tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
-                    tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
-                    logging_loss = tr_loss
-
-                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
-                    # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
-                    if not os.path.exists(output_dir):
-                        os.makedirs(output_dir)
-                    model_to_save = (
-                        model.module if hasattr(model, "module") else model
-                    )  # Take care of distributed/parallel training
-                    model_to_save.save_pretrained(output_dir)
-                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
-                    logger.info("Saving model checkpoint to %s", output_dir)
-
-            if args.max_steps > 0 and global_step > args.max_steps:
-                epoch_iterator.close()
-                break
-        if args.max_steps > 0 and global_step > args.max_steps:
-            train_iterator.close()
-            break
-
-    if args.local_rank in [-1, 0]:
-        tb_writer.close()
-
-    return global_step, tr_loss / global_step
-
-
-def evaluate(args, model, tokenizer, prefix=""):
-    dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
-
-    if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-        os.makedirs(args.output_dir)
-
-    args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
-    # Note that DistributedSampler samples randomly
-    eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
-    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
-    # Eval!
-    logger.info("***** Running evaluation {} *****".format(prefix))
-    logger.info("  Num examples = %d", len(dataset))
-    logger.info("  Batch size = %d", args.eval_batch_size)
-    all_results = []
-    for batch in tqdm(eval_dataloader, desc="Evaluating"):
-        model.eval()
-        batch = tuple(t.to(args.device) for t in batch)
-        with torch.no_grad():
-            inputs = {"input_ids": batch[0], "attention_mask": batch[1]}
-            if args.model_type != "distilbert":
-                inputs["token_type_ids"] = None if args.model_type == "xlm" else batch[2]  # XLM don't use segment_ids
-            example_indices = batch[3]
-            if args.model_type in ["xlnet", "xlm"]:
-                inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
-            outputs = model(**inputs)
-
-        for i, example_index in enumerate(example_indices):
-            eval_feature = features[example_index.item()]
-            unique_id = int(eval_feature.unique_id)
-            if args.model_type in ["xlnet", "xlm"]:
-                # XLNet uses a more complex post-processing procedure
-                result = RawResultExtended(
-                    unique_id=unique_id,
-                    start_top_log_probs=to_list(outputs[0][i]),
-                    start_top_index=to_list(outputs[1][i]),
-                    end_top_log_probs=to_list(outputs[2][i]),
-                    end_top_index=to_list(outputs[3][i]),
-                    cls_logits=to_list(outputs[4][i]),
-                )
-            else:
-                result = RawResult(
-                    unique_id=unique_id, start_logits=to_list(outputs[0][i]), end_logits=to_list(outputs[1][i])
-                )
-            all_results.append(result)
-
-    # Compute predictions
-    output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
-    output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
-    if args.version_2_with_negative:
-        output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
-    else:
-        output_null_log_odds_file = None
-
-    if args.model_type in ["xlnet", "xlm"]:
-        # XLNet uses a more complex post-processing procedure
-        write_predictions_extended(
-            examples,
-            features,
-            all_results,
-            args.n_best_size,
-            args.max_answer_length,
-            output_prediction_file,
-            output_nbest_file,
-            output_null_log_odds_file,
-            args.predict_file,
-            model.config.start_n_top,
-            model.config.end_n_top,
-            args.version_2_with_negative,
-            tokenizer,
-            args.verbose_logging,
-        )
-    else:
-        write_predictions(
-            examples,
-            features,
-            all_results,
-            args.n_best_size,
-            args.max_answer_length,
-            args.do_lower_case,
-            output_prediction_file,
-            output_nbest_file,
-            output_null_log_odds_file,
-            args.verbose_logging,
-            args.version_2_with_negative,
-            args.null_score_diff_threshold,
-        )
-
-    # Evaluate with the official SQuAD script
-    evaluate_options = EVAL_OPTS(
-        data_file=args.predict_file, pred_file=output_prediction_file, na_prob_file=output_null_log_odds_file
-    )
-    results = evaluate_on_squad(evaluate_options)
-    return results
-
-
-def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
-    if args.local_rank not in [-1, 0] and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset,
-        # and the others will use the cache
-
-    # Load data features from cache or dataset file
-    input_file = args.predict_file if evaluate else args.train_file
-    cached_features_file = os.path.join(
-        os.path.dirname(input_file),
-        "cached_{}_{}_{}".format(
-            "dev" if evaluate else "train",
-            list(filter(None, args.model_name_or_path.split("/"))).pop(),
-            str(args.max_seq_length),
-        ),
-    )
-    if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
-        logger.info("Loading features from cached file %s", cached_features_file)
-        features = torch.load(cached_features_file)
-    else:
-        logger.info("Creating features from dataset file at %s", input_file)
-        examples = read_squad_examples(
-            input_file=input_file, is_training=not evaluate, version_2_with_negative=args.version_2_with_negative
-        )
-        features = convert_examples_to_features(
-            examples=examples,
-            tokenizer=tokenizer,
-            max_seq_length=args.max_seq_length,
-            doc_stride=args.doc_stride,
-            max_query_length=args.max_query_length,
-            is_training=not evaluate,
-        )
-        if args.local_rank in [-1, 0]:
-            logger.info("Saving features into cached file %s", cached_features_file)
-            torch.save(features, cached_features_file)
-
-    if args.local_rank == 0 and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset,
-        # and the others will use the cache
-
-    # Convert to Tensors and build dataset
-    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
-    all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
-    all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
-    all_cls_index = torch.tensor([f.cls_index for f in features], dtype=torch.long)
-    all_p_mask = torch.tensor([f.p_mask for f in features], dtype=torch.float)
-    if evaluate:
-        all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
-        dataset = TensorDataset(
-            all_input_ids, all_input_mask, all_segment_ids, all_example_index, all_cls_index, all_p_mask
-        )
-    else:
-        all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
-        all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
-        dataset = TensorDataset(
-            all_input_ids,
-            all_input_mask,
-            all_segment_ids,
-            all_start_positions,
-            all_end_positions,
-            all_cls_index,
-            all_p_mask,
-        )
-
-    if output_examples:
-        return dataset, examples, features
-    return dataset
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    # Required parameters
-    parser.add_argument(
-        "--train_file", default=None, type=str, required=True, help="SQuAD json for training. E.g., train-v1.1.json"
-    )
-    parser.add_argument(
-        "--predict_file",
-        default=None,
-        type=str,
-        required=True,
-        help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json",
-    )
-    parser.add_argument(
-        "--model_type",
-        default=None,
-        type=str,
-        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_TYPES),
-    )
-    parser.add_argument(
-        "--model_name_or_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models",
-    )
-    parser.add_argument(
-        "--output_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The output directory where the model checkpoints and predictions will be written.",
-    )
-
-    # Other parameters
-    parser.add_argument(
-        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        default="",
-        type=str,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        default="",
-        type=str,
-        help="Where do you want to store the pre-trained models downloaded from s3",
-    )
-
-    parser.add_argument(
-        "--version_2_with_negative",
-        action="store_true",
-        help="If true, the SQuAD examples contain some that do not have an answer.",
-    )
-    parser.add_argument(
-        "--null_score_diff_threshold",
-        type=float,
-        default=0.0,
-        help="If null_score - best_non_null is greater than the threshold predict null.",
-    )
-
-    parser.add_argument(
-        "--max_seq_length",
-        default=384,
-        type=int,
-        help="The maximum total input sequence length after WordPiece tokenization. Sequences "
-        "longer than this will be truncated, and sequences shorter than this will be padded.",
-    )
-    parser.add_argument(
-        "--doc_stride",
-        default=128,
-        type=int,
-        help="When splitting up a long document into chunks, how much stride to take between chunks.",
-    )
-    parser.add_argument(
-        "--max_query_length",
-        default=64,
-        type=int,
-        help="The maximum number of tokens for the question. Questions longer than this will "
-        "be truncated to this length.",
-    )
-    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
-    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
-    parser.add_argument(
-        "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
-    )
-    parser.add_argument(
-        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
-    )
-
-    parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
-    parser.add_argument(
-        "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
-    )
-    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight deay if we apply some.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument(
-        "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
-    )
-    parser.add_argument(
-        "--max_steps",
-        default=-1,
-        type=int,
-        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
-    )
-    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
-    parser.add_argument(
-        "--n_best_size",
-        default=20,
-        type=int,
-        help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
-    )
-    parser.add_argument(
-        "--max_answer_length",
-        default=30,
-        type=int,
-        help="The maximum length of an answer that can be generated. This is needed because the start "
-        "and end predictions are not conditioned on one another.",
-    )
-    parser.add_argument(
-        "--verbose_logging",
-        action="store_true",
-        help="If true, all of the warnings related to data processing will be printed. "
-        "A number of warnings are expected for a normal SQuAD evaluation.",
-    )
-
-    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
-    parser.add_argument(
-        "--eval_all_checkpoints",
-        action="store_true",
-        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
-    )
-    parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
-    parser.add_argument(
-        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
-    )
-    parser.add_argument(
-        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
-    )
-    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
-
-    parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
-    parser.add_argument(
-        "--fp16",
-        action="store_true",
-        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
-    )
-    parser.add_argument(
-        "--fp16_opt_level",
-        type=str,
-        default="O1",
-        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-        "See details at https://nvidia.github.io/apex/amp.html",
-    )
-    parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
-    parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
-    args = parser.parse_args()
-
-    if (
-        os.path.exists(args.output_dir)
-        and os.listdir(args.output_dir)
-        and args.do_train
-        and not args.overwrite_output_dir
-    ):
-        raise ValueError(
-            "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
-                args.output_dir
-            )
-        )
-
-    # Setup distant debugging if needed
-    if args.server_ip and args.server_port:
-        # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
-        import ptvsd
-
-        print("Waiting for debugger attach")
-        ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
-        ptvsd.wait_for_attach()
-
-    # Setup CUDA, GPU & distributed training
-    if args.local_rank == -1 or args.no_cuda:
-        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
-    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
-        torch.cuda.set_device(args.local_rank)
-        device = torch.device("cuda", args.local_rank)
-        torch.distributed.init_process_group(backend="nccl")
-        args.n_gpu = 1
-    args.device = device
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
-    )
-    logger.warning(
-        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        args.local_rank,
-        device,
-        args.n_gpu,
-        bool(args.local_rank != -1),
-        args.fp16,
-    )
-
-    # Set seed
-    set_seed(args)
-
-    # Load pretrained model and tokenizer
-    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will
-        # download model & vocab
-
-    args.model_type = args.model_type.lower()
-    config = AutoConfig.from_pretrained(
-        args.config_name if args.config_name else args.model_name_or_path,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-    tokenizer = AutoTokenizer.from_pretrained(
-        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
-        do_lower_case=args.do_lower_case,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-    model = AutoModelForQuestionAnswering.from_pretrained(
-        args.model_name_or_path,
-        from_tf=bool(".ckpt" in args.model_name_or_path),
-        config=config,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-
-    if args.local_rank == 0:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will
-        # download model & vocab
-
-    model.to(args.device)
-
-    logger.info("Training/evaluation parameters %s", args)
-
-    # Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum
-    # if args.fp16 is set. Otherwise it'll default to "promote" mode, and we'll get fp32 operations.
-    # Note that running `--fp16_opt_level="O2"` will remove the need for this code, but it is still valid.
-    if args.fp16:
-        try:
-            import apex
-
-            apex.amp.register_half_function(torch, "einsum")
-        except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-
-    # Training
-    if args.do_train:
-        train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
-        global_step, tr_loss = train(args, train_dataset, model, tokenizer)
-        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-
-    # Save the trained model and the tokenizer
-    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        logger.info("Saving model checkpoint to %s", args.output_dir)
-        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
-        # They can then be reloaded using `from_pretrained()`
-        model_to_save = (
-            model.module if hasattr(model, "module") else model
-        )  # Take care of distributed/parallel training
-        model_to_save.save_pretrained(args.output_dir)
-        tokenizer.save_pretrained(args.output_dir)
-
-        # Good practice: save your training arguments together with the trained model
-        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
-
-        # Load a trained model and vocabulary that you have fine-tuned
-        model = AutoModelForQuestionAnswering.from_pretrained(args.output_dir)
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
-        model.to(args.device)
-
-    # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
-    results = {}
-    if args.do_eval and args.local_rank in [-1, 0]:
-        checkpoints = [args.output_dir]
-        if args.eval_all_checkpoints:
-            checkpoints = list(
-                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
-            )
-
-        logger.info("Evaluate the following checkpoints: %s", checkpoints)
-
-        for checkpoint in checkpoints:
-            # Reload the model
-            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
-            model = AutoModelForQuestionAnswering.from_pretrained(checkpoint)
-            model.to(args.device)
-
-            # Evaluate
-            result = evaluate(args, model, tokenizer, prefix=global_step)
-
-            result = dict((k + ("_{}".format(global_step) if global_step else ""), v) for k, v in result.items())
-            results.update(result)
-
-    logger.info("Results: {}".format(results))
-
-    return results
-
-
-if __name__ == "__main__":
-    main()

templates/adding_a_new_example_script/utils_xxx.py

-# coding=utf-8
-# Copyright 2018 XXX.  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.
-""" Load XXX dataset. """
-
-
-import collections
-import json
-import logging
-import math
-
-from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
-
-# Required by XLNet evaluation method to compute optimal threshold (see write_predictions_extended() method)
-from utils_squad_evaluate import find_all_best_thresh_v2, get_raw_scores, make_qid_to_has_ans
-
-
-logger = logging.getLogger(__name__)
-
-
-class SquadExample(object):
-    """
-    A single training/test example for the Squad dataset.
-    For examples without an answer, the start and end position are -1.
-    """
-
-    def __init__(
-        self,
-        qas_id,
-        question_text,
-        doc_tokens,
-        orig_answer_text=None,
-        start_position=None,
-        end_position=None,
-        is_impossible=None,
-    ):
-        self.qas_id = qas_id
-        self.question_text = question_text
-        self.doc_tokens = doc_tokens
-        self.orig_answer_text = orig_answer_text
-        self.start_position = start_position
-        self.end_position = end_position
-        self.is_impossible = is_impossible
-
-    def __str__(self):
-        return self.__repr__()
-
-    def __repr__(self):
-        s = ""
-        s += "qas_id: %s" % (self.qas_id)
-        s += ", question_text: %s" % (self.question_text)
-        s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
-        if self.start_position:
-            s += ", start_position: %d" % (self.start_position)
-        if self.end_position:
-            s += ", end_position: %d" % (self.end_position)
-        if self.is_impossible:
-            s += ", is_impossible: %r" % (self.is_impossible)
-        return s
-
-
-class InputFeatures(object):
-    """A single set of features of data."""
-
-    def __init__(
-        self,
-        unique_id,
-        example_index,
-        doc_span_index,
-        tokens,
-        token_to_orig_map,
-        token_is_max_context,
-        input_ids,
-        input_mask,
-        segment_ids,
-        cls_index,
-        p_mask,
-        paragraph_len,
-        start_position=None,
-        end_position=None,
-        is_impossible=None,
-    ):
-        self.unique_id = unique_id
-        self.example_index = example_index
-        self.doc_span_index = doc_span_index
-        self.tokens = tokens
-        self.token_to_orig_map = token_to_orig_map
-        self.token_is_max_context = token_is_max_context
-        self.input_ids = input_ids
-        self.input_mask = input_mask
-        self.segment_ids = segment_ids
-        self.cls_index = cls_index
-        self.p_mask = p_mask
-        self.paragraph_len = paragraph_len
-        self.start_position = start_position
-        self.end_position = end_position
-        self.is_impossible = is_impossible
-
-
-def read_squad_examples(input_file, is_training, version_2_with_negative):
-    """Read a SQuAD json file into a list of SquadExample."""
-    with open(input_file, "r", encoding="utf-8") as reader:
-        input_data = json.load(reader)["data"]
-
-    def is_whitespace(c):
-        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
-            return True
-        return False
-
-    examples = []
-    for entry in input_data:
-        for paragraph in entry["paragraphs"]:
-            paragraph_text = paragraph["context"]
-            doc_tokens = []
-            char_to_word_offset = []
-            prev_is_whitespace = True
-            for c in paragraph_text:
-                if is_whitespace(c):
-                    prev_is_whitespace = True
-                else:
-                    if prev_is_whitespace:
-                        doc_tokens.append(c)
-                    else:
-                        doc_tokens[-1] += c
-                    prev_is_whitespace = False
-                char_to_word_offset.append(len(doc_tokens) - 1)
-
-            for qa in paragraph["qas"]:
-                qas_id = qa["id"]
-                question_text = qa["question"]
-                start_position = None
-                end_position = None
-                orig_answer_text = None
-                is_impossible = False
-                if is_training:
-                    if version_2_with_negative:
-                        is_impossible = qa["is_impossible"]
-                    if (len(qa["answers"]) != 1) and (not is_impossible):
-                        raise ValueError("For training, each question should have exactly 1 answer.")
-                    if not is_impossible:
-                        answer = qa["answers"][0]
-                        orig_answer_text = answer["text"]
-                        answer_offset = answer["answer_start"]
-                        answer_length = len(orig_answer_text)
-                        start_position = char_to_word_offset[answer_offset]
-                        end_position = char_to_word_offset[answer_offset + answer_length - 1]
-                        # Only add answers where the text can be exactly recovered from the
-                        # document. If this CAN'T happen it's likely due to weird Unicode
-                        # stuff so we will just skip the example.
-                        #
-                        # Note that this means for training mode, every example is NOT
-                        # guaranteed to be preserved.
-                        actual_text = " ".join(doc_tokens[start_position : (end_position + 1)])
-                        cleaned_answer_text = " ".join(whitespace_tokenize(orig_answer_text))
-                        if actual_text.find(cleaned_answer_text) == -1:
-                            logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text)
-                            continue
-                    else:
-                        start_position = -1
-                        end_position = -1
-                        orig_answer_text = ""
-
-                example = SquadExample(
-                    qas_id=qas_id,
-                    question_text=question_text,
-                    doc_tokens=doc_tokens,
-                    orig_answer_text=orig_answer_text,
-                    start_position=start_position,
-                    end_position=end_position,
-                    is_impossible=is_impossible,
-                )
-                examples.append(example)
-    return examples
-
-
-def convert_examples_to_features(
-    examples,
-    tokenizer,
-    max_seq_length,
-    doc_stride,
-    max_query_length,
-    is_training,
-    cls_token_at_end=False,
-    cls_token="[CLS]",
-    sep_token="[SEP]",
-    pad_token=0,
-    sequence_a_segment_id=0,
-    sequence_b_segment_id=1,
-    cls_token_segment_id=0,
-    pad_token_segment_id=0,
-    mask_padding_with_zero=True,
-):
-    """Loads a data file into a list of `InputBatch`s."""
-
-    unique_id = 1000000000
-    # cnt_pos, cnt_neg = 0, 0
-    # max_N, max_M = 1024, 1024
-    # f = np.zeros((max_N, max_M), dtype=np.float32)
-
-    features = []
-    for (example_index, example) in enumerate(examples):
-
-        # if example_index % 100 == 0:
-        #     logger.info('Converting %s/%s pos %s neg %s', example_index, len(examples), cnt_pos, cnt_neg)
-
-        query_tokens = tokenizer.tokenize(example.question_text)
-
-        if len(query_tokens) > max_query_length:
-            query_tokens = query_tokens[0:max_query_length]
-
-        tok_to_orig_index = []
-        orig_to_tok_index = []
-        all_doc_tokens = []
-        for (i, token) in enumerate(example.doc_tokens):
-            orig_to_tok_index.append(len(all_doc_tokens))
-            sub_tokens = tokenizer.tokenize(token)
-            for sub_token in sub_tokens:
-                tok_to_orig_index.append(i)
-                all_doc_tokens.append(sub_token)
-
-        tok_start_position = None
-        tok_end_position = None
-        if is_training and example.is_impossible:
-            tok_start_position = -1
-            tok_end_position = -1
-        if is_training and not example.is_impossible:
-            tok_start_position = orig_to_tok_index[example.start_position]
-            if example.end_position < len(example.doc_tokens) - 1:
-                tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
-            else:
-                tok_end_position = len(all_doc_tokens) - 1
-            (tok_start_position, tok_end_position) = _improve_answer_span(
-                all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.orig_answer_text
-            )
-
-        # The -3 accounts for [CLS], [SEP] and [SEP]
-        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
-
-        # We can have documents that are longer than the maximum sequence length.
-        # To deal with this we do a sliding window approach, where we take chunks
-        # of the up to our max length with a stride of `doc_stride`.
-        _DocSpan = collections.namedtuple("DocSpan", ["start", "length"])  # pylint: disable=invalid-name
-        doc_spans = []
-        start_offset = 0
-        while start_offset < len(all_doc_tokens):
-            length = len(all_doc_tokens) - start_offset
-            if length > max_tokens_for_doc:
-                length = max_tokens_for_doc
-            doc_spans.append(_DocSpan(start=start_offset, length=length))
-            if start_offset + length == len(all_doc_tokens):
-                break
-            start_offset += min(length, doc_stride)
-
-        for (doc_span_index, doc_span) in enumerate(doc_spans):
-            tokens = []
-            token_to_orig_map = {}
-            token_is_max_context = {}
-            segment_ids = []
-
-            # p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
-            # Original TF implem also keep the classification token (set to 0) (not sure why...)
-            p_mask = []
-
-            # CLS token at the beginning
-            if not cls_token_at_end:
-                tokens.append(cls_token)
-                segment_ids.append(cls_token_segment_id)
-                p_mask.append(0)
-                cls_index = 0
-
-            # Query
-            for token in query_tokens:
-                tokens.append(token)
-                segment_ids.append(sequence_a_segment_id)
-                p_mask.append(1)
-
-            # SEP token
-            tokens.append(sep_token)
-            segment_ids.append(sequence_a_segment_id)
-            p_mask.append(1)
-
-            # Paragraph
-            for i in range(doc_span.length):
-                split_token_index = doc_span.start + i
-                token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
-
-                is_max_context = _check_is_max_context(doc_spans, doc_span_index, split_token_index)
-                token_is_max_context[len(tokens)] = is_max_context
-                tokens.append(all_doc_tokens[split_token_index])
-                segment_ids.append(sequence_b_segment_id)
-                p_mask.append(0)
-            paragraph_len = doc_span.length
-
-            # SEP token
-            tokens.append(sep_token)
-            segment_ids.append(sequence_b_segment_id)
-            p_mask.append(1)
-
-            # CLS token at the end
-            if cls_token_at_end:
-                tokens.append(cls_token)
-                segment_ids.append(cls_token_segment_id)
-                p_mask.append(0)
-                cls_index = len(tokens) - 1  # Index of classification token
-
-            input_ids = tokenizer.convert_tokens_to_ids(tokens)
-
-            # The mask has 1 for real tokens and 0 for padding tokens. Only real
-            # tokens are attended to.
-            input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
-
-            # Zero-pad up to the sequence length.
-            while len(input_ids) < max_seq_length:
-                input_ids.append(pad_token)
-                input_mask.append(0 if mask_padding_with_zero else 1)
-                segment_ids.append(pad_token_segment_id)
-                p_mask.append(1)
-
-            assert (
-                len(input_ids) == max_seq_length
-            ), f"Input ids and sequence have mismatched lengths {len(input_ids)} and {max_seq_length}"
-            assert (
-                len(input_mask) == max_seq_length
-            ), f"Input mask and sequence have mismatched lengths {len(input_mask)} and {max_seq_length}"
-            assert (
-                len(segment_ids) == max_seq_length
-            ), f"Segment ids and sequence have mismatched lengths {len(segment_ids)} and {max_seq_length}"
-
-            span_is_impossible = example.is_impossible
-            start_position = None
-            end_position = None
-            if is_training and not span_is_impossible:
-                # For training, if our document chunk does not contain an annotation
-                # we throw it out, since there is nothing to predict.
-                doc_start = doc_span.start
-                doc_end = doc_span.start + doc_span.length - 1
-                out_of_span = False
-                if not (tok_start_position >= doc_start and tok_end_position <= doc_end):
-                    out_of_span = True
-                if out_of_span:
-                    start_position = 0
-                    end_position = 0
-                    span_is_impossible = True
-                else:
-                    doc_offset = len(query_tokens) + 2
-                    start_position = tok_start_position - doc_start + doc_offset
-                    end_position = tok_end_position - doc_start + doc_offset
-
-            if is_training and span_is_impossible:
-                start_position = cls_index
-                end_position = cls_index
-
-            if example_index < 20:
-                logger.info("*** Example ***")
-                logger.info("unique_id: %s" % (unique_id))
-                logger.info("example_index: %s" % (example_index))
-                logger.info("doc_span_index: %s" % (doc_span_index))
-                logger.info("tokens: %s" % " ".join(tokens))
-                logger.info(
-                    "token_to_orig_map: %s" % " ".join(["%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()])
-                )
-                logger.info(
-                    "token_is_max_context: %s"
-                    % " ".join(["%d:%s" % (x, y) for (x, y) in token_is_max_context.items()])
-                )
-                logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
-                logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
-                logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
-                if is_training and span_is_impossible:
-                    logger.info("impossible example")
-                if is_training and not span_is_impossible:
-                    answer_text = " ".join(tokens[start_position : (end_position + 1)])
-                    logger.info("start_position: %d" % (start_position))
-                    logger.info("end_position: %d" % (end_position))
-                    logger.info("answer: %s" % (answer_text))
-
-            features.append(
-                InputFeatures(
-                    unique_id=unique_id,
-                    example_index=example_index,
-                    doc_span_index=doc_span_index,
-                    tokens=tokens,
-                    token_to_orig_map=token_to_orig_map,
-                    token_is_max_context=token_is_max_context,
-                    input_ids=input_ids,
-                    input_mask=input_mask,
-                    segment_ids=segment_ids,
-                    cls_index=cls_index,
-                    p_mask=p_mask,
-                    paragraph_len=paragraph_len,
-                    start_position=start_position,
-                    end_position=end_position,
-                    is_impossible=span_is_impossible,
-                )
-            )
-            unique_id += 1
-
-    return features
-
-
-def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
-    """Returns tokenized answer spans that better match the annotated answer."""
-
-    # The SQuAD annotations are character based. We first project them to
-    # whitespace-tokenized words. But then after WordPiece tokenization, we can
-    # often find a "better match". For example:
-    #
-    #   Question: What year was John Smith born?
-    #   Context: The leader was John Smith (1895-1943).
-    #   Answer: 1895
-    #
-    # The original whitespace-tokenized answer will be "(1895-1943).". However
-    # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
-    # the exact answer, 1895.
-    #
-    # However, this is not always possible. Consider the following:
-    #
-    #   Question: What country is the top exporter of electronics?
-    #   Context: The Japanese electronics industry is the largest in the world.
-    #   Answer: Japan
-    #
-    # In this case, the annotator chose "Japan" as a character sub-span of
-    # the word "Japanese". Since our WordPiece tokenizer does not split
-    # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
-    # in SQuAD, but does happen.
-    tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
-
-    for new_start in range(input_start, input_end + 1):
-        for new_end in range(input_end, new_start - 1, -1):
-            text_span = " ".join(doc_tokens[new_start : (new_end + 1)])
-            if text_span == tok_answer_text:
-                return (new_start, new_end)
-
-    return (input_start, input_end)
-
-
-def _check_is_max_context(doc_spans, cur_span_index, position):
-    """Check if this is the 'max context' doc span for the token."""
-
-    # Because of the sliding window approach taken to scoring documents, a single
-    # token can appear in multiple documents. E.g.
-    #  Doc: the man went to the store and bought a gallon of milk
-    #  Span A: the man went to the
-    #  Span B: to the store and bought
-    #  Span C: and bought a gallon of
-    #  ...
-    #
-    # Now the word 'bought' will have two scores from spans B and C. We only
-    # want to consider the score with "maximum context", which we define as
-    # the *minimum* of its left and right context (the *sum* of left and
-    # right context will always be the same, of course).
-    #
-    # In the example the maximum context for 'bought' would be span C since
-    # it has 1 left context and 3 right context, while span B has 4 left context
-    # and 0 right context.
-    best_score = None
-    best_span_index = None
-    for (span_index, doc_span) in enumerate(doc_spans):
-        end = doc_span.start + doc_span.length - 1
-        if position < doc_span.start:
-            continue
-        if position > end:
-            continue
-        num_left_context = position - doc_span.start
-        num_right_context = end - position
-        score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
-        if best_score is None or score > best_score:
-            best_score = score
-            best_span_index = span_index
-
-    return cur_span_index == best_span_index
-
-
-RawResult = collections.namedtuple("RawResult", ["unique_id", "start_logits", "end_logits"])
-
-
-def write_predictions(
-    all_examples,
-    all_features,
-    all_results,
-    n_best_size,
-    max_answer_length,
-    do_lower_case,
-    output_prediction_file,
-    output_nbest_file,
-    output_null_log_odds_file,
-    verbose_logging,
-    version_2_with_negative,
-    null_score_diff_threshold,
-):
-    """Write final predictions to the json file and log-odds of null if needed."""
-    logger.info("Writing predictions to: %s" % (output_prediction_file))
-    logger.info("Writing nbest to: %s" % (output_nbest_file))
-
-    example_index_to_features = collections.defaultdict(list)
-    for feature in all_features:
-        example_index_to_features[feature.example_index].append(feature)
-
-    unique_id_to_result = {}
-    for result in all_results:
-        unique_id_to_result[result.unique_id] = result
-
-    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-        "PrelimPrediction", ["feature_index", "start_index", "end_index", "start_logit", "end_logit"]
-    )
-
-    all_predictions = collections.OrderedDict()
-    all_nbest_json = collections.OrderedDict()
-    scores_diff_json = collections.OrderedDict()
-
-    for (example_index, example) in enumerate(all_examples):
-        features = example_index_to_features[example_index]
-
-        prelim_predictions = []
-        # keep track of the minimum score of null start+end of position 0
-        score_null = 1000000  # large and positive
-        min_null_feature_index = 0  # the paragraph slice with min null score
-        null_start_logit = 0  # the start logit at the slice with min null score
-        null_end_logit = 0  # the end logit at the slice with min null score
-        for (feature_index, feature) in enumerate(features):
-            result = unique_id_to_result[feature.unique_id]
-            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
-            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
-            # if we could have irrelevant answers, get the min score of irrelevant
-            if version_2_with_negative:
-                feature_null_score = result.start_logits[0] + result.end_logits[0]
-                if feature_null_score < score_null:
-                    score_null = feature_null_score
-                    min_null_feature_index = feature_index
-                    null_start_logit = result.start_logits[0]
-                    null_end_logit = result.end_logits[0]
-            for start_index in start_indexes:
-                for end_index in end_indexes:
-                    # We could hypothetically create invalid predictions, e.g., predict
-                    # that the start of the span is in the question. We throw out all
-                    # invalid predictions.
-                    if start_index >= len(feature.tokens):
-                        continue
-                    if end_index >= len(feature.tokens):
-                        continue
-                    if start_index not in feature.token_to_orig_map:
-                        continue
-                    if end_index not in feature.token_to_orig_map:
-                        continue
-                    if not feature.token_is_max_context.get(start_index, False):
-                        continue
-                    if end_index < start_index:
-                        continue
-                    length = end_index - start_index + 1
-                    if length > max_answer_length:
-                        continue
-                    prelim_predictions.append(
-                        _PrelimPrediction(
-                            feature_index=feature_index,
-                            start_index=start_index,
-                            end_index=end_index,
-                            start_logit=result.start_logits[start_index],
-                            end_logit=result.end_logits[end_index],
-                        )
-                    )
-        if version_2_with_negative:
-            prelim_predictions.append(
-                _PrelimPrediction(
-                    feature_index=min_null_feature_index,
-                    start_index=0,
-                    end_index=0,
-                    start_logit=null_start_logit,
-                    end_logit=null_end_logit,
-                )
-            )
-        prelim_predictions = sorted(prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True)
-
-        _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-            "NbestPrediction", ["text", "start_logit", "end_logit"]
-        )
-
-        seen_predictions = {}
-        nbest = []
-        for pred in prelim_predictions:
-            if len(nbest) >= n_best_size:
-                break
-            feature = features[pred.feature_index]
-            if pred.start_index > 0:  # this is a non-null prediction
-                tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
-                orig_doc_start = feature.token_to_orig_map[pred.start_index]
-                orig_doc_end = feature.token_to_orig_map[pred.end_index]
-                orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
-                tok_text = " ".join(tok_tokens)
-
-                # De-tokenize WordPieces that have been split off.
-                tok_text = tok_text.replace(" ##", "")
-                tok_text = tok_text.replace("##", "")
-
-                # Clean whitespace
-                tok_text = tok_text.strip()
-                tok_text = " ".join(tok_text.split())
-                orig_text = " ".join(orig_tokens)
-
-                final_text = get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
-                if final_text in seen_predictions:
-                    continue
-
-                seen_predictions[final_text] = True
-            else:
-                final_text = ""
-                seen_predictions[final_text] = True
-
-            nbest.append(_NbestPrediction(text=final_text, start_logit=pred.start_logit, end_logit=pred.end_logit))
-        # if we didn't include the empty option in the n-best, include it
-        if version_2_with_negative:
-            if "" not in seen_predictions:
-                nbest.append(_NbestPrediction(text="", start_logit=null_start_logit, end_logit=null_end_logit))
-
-            # In very rare edge cases we could only have single null prediction.
-            # So we just create a nonce prediction in this case to avoid failure.
-            if len(nbest) == 1:
-                nbest.insert(0, _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
-
-        # In very rare edge cases we could have no valid predictions. So we
-        # just create a nonce prediction in this case to avoid failure.
-        if not nbest:
-            nbest.append(_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
-
-        assert len(nbest) >= 1, "No valid predictions"
-
-        total_scores = []
-        best_non_null_entry = None
-        for entry in nbest:
-            total_scores.append(entry.start_logit + entry.end_logit)
-            if not best_non_null_entry:
-                if entry.text:
-                    best_non_null_entry = entry
-
-        probs = _compute_softmax(total_scores)
-
-        nbest_json = []
-        for (i, entry) in enumerate(nbest):
-            output = collections.OrderedDict()
-            output["text"] = entry.text
-            output["probability"] = probs[i]
-            output["start_logit"] = entry.start_logit
-            output["end_logit"] = entry.end_logit
-            nbest_json.append(output)
-
-        assert len(nbest_json) >= 1, "No valid predictions"
-
-        if not version_2_with_negative:
-            all_predictions[example.qas_id] = nbest_json[0]["text"]
-        else:
-            # predict "" iff the null score - the score of best non-null > threshold
-            score_diff = score_null - best_non_null_entry.start_logit - (best_non_null_entry.end_logit)
-            scores_diff_json[example.qas_id] = score_diff
-            if score_diff > null_score_diff_threshold:
-                all_predictions[example.qas_id] = ""
-            else:
-                all_predictions[example.qas_id] = best_non_null_entry.text
-        all_nbest_json[example.qas_id] = nbest_json
-
-    with open(output_prediction_file, "w") as writer:
-        writer.write(json.dumps(all_predictions, indent=4) + "\n")
-
-    with open(output_nbest_file, "w") as writer:
-        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
-
-    if version_2_with_negative:
-        with open(output_null_log_odds_file, "w") as writer:
-            writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
-
-    return all_predictions
-
-
-# For XLNet (and XLM which uses the same head)
-RawResultExtended = collections.namedtuple(
-    "RawResultExtended",
-    ["unique_id", "start_top_log_probs", "start_top_index", "end_top_log_probs", "end_top_index", "cls_logits"],
-)
-
-
-def write_predictions_extended(
-    all_examples,
-    all_features,
-    all_results,
-    n_best_size,
-    max_answer_length,
-    output_prediction_file,
-    output_nbest_file,
-    output_null_log_odds_file,
-    orig_data_file,
-    start_n_top,
-    end_n_top,
-    version_2_with_negative,
-    tokenizer,
-    verbose_logging,
-):
-    """XLNet write prediction logic (more complex than Bert's).
-    Write final predictions to the json file and log-odds of null if needed.
-
-    Requires utils_squad_evaluate.py
-    """
-    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-        "PrelimPrediction", ["feature_index", "start_index", "end_index", "start_log_prob", "end_log_prob"]
-    )
-
-    _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
-        "NbestPrediction", ["text", "start_log_prob", "end_log_prob"]
-    )
-
-    logger.info("Writing predictions to: %s", output_prediction_file)
-    # logger.info("Writing nbest to: %s" % (output_nbest_file))
-
-    example_index_to_features = collections.defaultdict(list)
-    for feature in all_features:
-        example_index_to_features[feature.example_index].append(feature)
-
-    unique_id_to_result = {}
-    for result in all_results:
-        unique_id_to_result[result.unique_id] = result
-
-    all_predictions = collections.OrderedDict()
-    all_nbest_json = collections.OrderedDict()
-    scores_diff_json = collections.OrderedDict()
-
-    for (example_index, example) in enumerate(all_examples):
-        features = example_index_to_features[example_index]
-
-        prelim_predictions = []
-        # keep track of the minimum score of null start+end of position 0
-        score_null = 1000000  # large and positive
-
-        for (feature_index, feature) in enumerate(features):
-            result = unique_id_to_result[feature.unique_id]
-
-            cur_null_score = result.cls_logits
-
-            # if we could have irrelevant answers, get the min score of irrelevant
-            score_null = min(score_null, cur_null_score)
-
-            for i in range(start_n_top):
-                for j in range(end_n_top):
-                    start_log_prob = result.start_top_log_probs[i]
-                    start_index = result.start_top_index[i]
-
-                    j_index = i * end_n_top + j
-
-                    end_log_prob = result.end_top_log_probs[j_index]
-                    end_index = result.end_top_index[j_index]
-
-                    # We could hypothetically create invalid predictions, e.g., predict
-                    # that the start of the span is in the question. We throw out all
-                    # invalid predictions.
-                    if start_index >= feature.paragraph_len - 1:
-                        continue
-                    if end_index >= feature.paragraph_len - 1:
-                        continue
-
-                    if not feature.token_is_max_context.get(start_index, False):
-                        continue
-                    if end_index < start_index:
-                        continue
-                    length = end_index - start_index + 1
-                    if length > max_answer_length:
-                        continue
-
-                    prelim_predictions.append(
-                        _PrelimPrediction(
-                            feature_index=feature_index,
-                            start_index=start_index,
-                            end_index=end_index,
-                            start_log_prob=start_log_prob,
-                            end_log_prob=end_log_prob,
-                        )
-                    )
-
-        prelim_predictions = sorted(
-            prelim_predictions, key=lambda x: (x.start_log_prob + x.end_log_prob), reverse=True
-        )
-
-        seen_predictions = {}
-        nbest = []
-        for pred in prelim_predictions:
-            if len(nbest) >= n_best_size:
-                break
-            feature = features[pred.feature_index]
-
-            # XLNet un-tokenizer
-            # Let's keep it simple for now and see if we need all this later.
-            #
-            # tok_start_to_orig_index = feature.tok_start_to_orig_index
-            # tok_end_to_orig_index = feature.tok_end_to_orig_index
-            # start_orig_pos = tok_start_to_orig_index[pred.start_index]
-            # end_orig_pos = tok_end_to_orig_index[pred.end_index]
-            # paragraph_text = example.paragraph_text
-            # final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
-
-            # Previously used Bert untokenizer
-            tok_tokens = feature.tokens[pred.start_index : (pred.end_index + 1)]
-            orig_doc_start = feature.token_to_orig_map[pred.start_index]
-            orig_doc_end = feature.token_to_orig_map[pred.end_index]
-            orig_tokens = example.doc_tokens[orig_doc_start : (orig_doc_end + 1)]
-            tok_text = tokenizer.convert_tokens_to_string(tok_tokens)
-
-            # Clean whitespace
-            tok_text = tok_text.strip()
-            tok_text = " ".join(tok_text.split())
-            orig_text = " ".join(orig_tokens)
-
-            final_text = get_final_text(tok_text, orig_text, tokenizer.do_lower_case, verbose_logging)
-
-            if final_text in seen_predictions:
-                continue
-
-            seen_predictions[final_text] = True
-
-            nbest.append(
-                _NbestPrediction(text=final_text, start_log_prob=pred.start_log_prob, end_log_prob=pred.end_log_prob)
-            )
-
-        # In very rare edge cases we could have no valid predictions. So we
-        # just create a nonce prediction in this case to avoid failure.
-        if not nbest:
-            nbest.append(_NbestPrediction(text="", start_log_prob=-1e6, end_log_prob=-1e6))
-
-        total_scores = []
-        best_non_null_entry = None
-        for entry in nbest:
-            total_scores.append(entry.start_log_prob + entry.end_log_prob)
-            if not best_non_null_entry:
-                best_non_null_entry = entry
-
-        probs = _compute_softmax(total_scores)
-
-        nbest_json = []
-        for (i, entry) in enumerate(nbest):
-            output = collections.OrderedDict()
-            output["text"] = entry.text
-            output["probability"] = probs[i]
-            output["start_log_prob"] = entry.start_log_prob
-            output["end_log_prob"] = entry.end_log_prob
-            nbest_json.append(output)
-
-        assert len(nbest_json) >= 1, "No valid predictions"
-        assert best_non_null_entry is not None, "No valid predictions"
-
-        score_diff = score_null
-        scores_diff_json[example.qas_id] = score_diff
-        # note(zhiliny): always predict best_non_null_entry
-        # and the evaluation script will search for the best threshold
-        all_predictions[example.qas_id] = best_non_null_entry.text
-
-        all_nbest_json[example.qas_id] = nbest_json
-
-    with open(output_prediction_file, "w") as writer:
-        writer.write(json.dumps(all_predictions, indent=4) + "\n")
-
-    with open(output_nbest_file, "w") as writer:
-        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
-
-    if version_2_with_negative:
-        with open(output_null_log_odds_file, "w") as writer:
-            writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
-
-    with open(orig_data_file, "r", encoding="utf-8") as reader:
-        orig_data = json.load(reader)["data"]
-
-    qid_to_has_ans = make_qid_to_has_ans(orig_data)
-    exact_raw, f1_raw = get_raw_scores(orig_data, all_predictions)
-    out_eval = {}
-
-    find_all_best_thresh_v2(out_eval, all_predictions, exact_raw, f1_raw, scores_diff_json, qid_to_has_ans)
-
-    return out_eval
-
-
-def get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
-    """Project the tokenized prediction back to the original text."""
-
-    # When we created the data, we kept track of the alignment between original
-    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
-    # now `orig_text` contains the span of our original text corresponding to the
-    # span that we predicted.
-    #
-    # However, `orig_text` may contain extra characters that we don't want in
-    # our prediction.
-    #
-    # For example, let's say:
-    #   pred_text = steve smith
-    #   orig_text = Steve Smith's
-    #
-    # We don't want to return `orig_text` because it contains the extra "'s".
-    #
-    # We don't want to return `pred_text` because it's already been normalized
-    # (the SQuAD eval script also does punctuation stripping/lower casing but
-    # our tokenizer does additional normalization like stripping accent
-    # characters).
-    #
-    # What we really want to return is "Steve Smith".
-    #
-    # Therefore, we have to apply a semi-complicated alignment heuristic between
-    # `pred_text` and `orig_text` to get a character-to-character alignment. This
-    # can fail in certain cases in which case we just return `orig_text`.
-
-    def _strip_spaces(text):
-        ns_chars = []
-        ns_to_s_map = collections.OrderedDict()
-        for (i, c) in enumerate(text):
-            if c == " ":
-                continue
-            ns_to_s_map[len(ns_chars)] = i
-            ns_chars.append(c)
-        ns_text = "".join(ns_chars)
-        return (ns_text, ns_to_s_map)
-
-    # We first tokenize `orig_text`, strip whitespace from the result
-    # and `pred_text`, and check if they are the same length. If they are
-    # NOT the same length, the heuristic has failed. If they are the same
-    # length, we assume the characters are one-to-one aligned.
-    tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
-
-    tok_text = " ".join(tokenizer.tokenize(orig_text))
-
-    start_position = tok_text.find(pred_text)
-    if start_position == -1:
-        if verbose_logging:
-            logger.info("Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
-        return orig_text
-    end_position = start_position + len(pred_text) - 1
-
-    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
-    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
-
-    if len(orig_ns_text) != len(tok_ns_text):
-        if verbose_logging:
-            logger.info("Length not equal after stripping spaces: '%s' vs '%s'", orig_ns_text, tok_ns_text)
-        return orig_text
-
-    # We then project the characters in `pred_text` back to `orig_text` using
-    # the character-to-character alignment.
-    tok_s_to_ns_map = {}
-    for (i, tok_index) in tok_ns_to_s_map.items():
-        tok_s_to_ns_map[tok_index] = i
-
-    orig_start_position = None
-    if start_position in tok_s_to_ns_map:
-        ns_start_position = tok_s_to_ns_map[start_position]
-        if ns_start_position in orig_ns_to_s_map:
-            orig_start_position = orig_ns_to_s_map[ns_start_position]
-
-    if orig_start_position is None:
-        if verbose_logging:
-            logger.info("Couldn't map start position")
-        return orig_text
-
-    orig_end_position = None
-    if end_position in tok_s_to_ns_map:
-        ns_end_position = tok_s_to_ns_map[end_position]
-        if ns_end_position in orig_ns_to_s_map:
-            orig_end_position = orig_ns_to_s_map[ns_end_position]
-
-    if orig_end_position is None:
-        if verbose_logging:
-            logger.info("Couldn't map end position")
-        return orig_text
-
-    output_text = orig_text[orig_start_position : (orig_end_position + 1)]
-    return output_text
-
-
-def _get_best_indexes(logits, n_best_size):
-    """Get the n-best logits from a list."""
-    index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
-
-    best_indexes = []
-    for i in range(len(index_and_score)):
-        if i >= n_best_size:
-            break
-        best_indexes.append(index_and_score[i][0])
-    return best_indexes
-
-
-def _compute_softmax(scores):
-    """Compute softmax probability over raw logits."""
-    if not scores:
-        return []
-
-    max_score = None
-    for score in scores:
-        if max_score is None or score > max_score:
-            max_score = score
-
-    exp_scores = []
-    total_sum = 0.0
-    for score in scores:
-        x = math.exp(score - max_score)
-        exp_scores.append(x)
-        total_sum += x
-
-    probs = []
-    for score in exp_scores:
-        probs.append(score / total_sum)
-    return probs

templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py

+# coding=utf-8
+# Copyright 2020 {{cookiecutter.authors}} 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 {{cookiecutter.example_name}}.
+"""
+# You can also adapt this script on your own {{cookiecutter.example_name}} task. Pointers for this are left as comments.
+
+import logging
+import math
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Optional
+
+from datasets import load_dataset
+
+import transformers
+from transformers import (
+    CONFIG_MAPPING,
+    MODEL_MAPPING,
+    AutoConfig,
+    {{cookiecutter.model_class}},
+    AutoTokenizer,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.trainer_utils import is_main_process
+
+
+logger = logging.getLogger(__name__)
+
+
+{%- if cookiecutter.can_train_from_scratch == "True" %}
+# You should update this to your particular problem to have better documentation of `model_type`
+MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
+MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The model checkpoint for weights initialization."
+            "Don't set if you want to train a model from scratch."
+        },
+    )
+    model_type: Optional[str] = field(
+        default=None,
+        metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
+    )
+    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 do you want to store the pretrained models downloaded from s3"}
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+{%- elif cookiecutter.can_train_from_scratch == "False" %}
+@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 do you want to store the pretrained models downloaded from s3"}
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+{% endif %}
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    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 text file)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text 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."},
+    )
+
+    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.")
+        else:
+            if self.train_file is not None:
+                extension = self.train_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
+            if self.validation_file is not None:
+                extension = self.validation_file.split(".")[-1]
+                assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
+
+
+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, TrainingArguments))
+    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 (
+        os.path.exists(training_args.output_dir)
+        and os.listdir(training_args.output_dir)
+        and training_args.do_train
+        and not training_args.overwrite_output_dir
+    ):
+        raise ValueError(
+            f"Output directory ({training_args.output_dir}) already exists and is not empty."
+            "Use --overwrite_output_dir to overcome."
+        )
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=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 CSV/JSON/TXT 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 CSV/JSON files, this script will use the column called 'text' or the first column. You can easily tweak this
+    # behavior (see below)
+    #
+    # 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
+        if data_args.validation_file is not None:
+            data_files["validation"] = data_args.train_file
+        extension = data_args.train_file.split(".")[-1]
+        if extension == "txt":
+            extension = "text"
+        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.
+{%- if cookiecutter.can_train_from_scratch == "True" %}
+    if model_args.config_name:
+        config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
+    elif model_args.model_name_or_path:
+        config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
+    else:
+        config = CONFIG_MAPPING[model_args.model_type]()
+        logger.warning("You are instantiating a new config instance from scratch.")
+
+    if model_args.tokenizer_name:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
+        )
+    elif model_args.model_name_or_path:
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
+        )
+    else:
+        raise ValueError(
+            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
+            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
+        )
+
+    if model_args.model_name_or_path:
+        model = {{cookiecutter.model_class}}.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,
+        )
+    else:
+        logger.info("Training new model from scratch")
+        model = {{cookiecutter.model_class}}.from_config(config)
+
+    model.resize_token_embeddings(len(tokenizer))
+{%- elif cookiecutter.can_train_from_scratch == "False" %}
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        num_labels=num_labels,
+        finetuning_task=data_args.task_name,
+        cache_dir=model_args.cache_dir,
+    )
+    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,
+    )
+    model = AutoModelForSequenceClassification.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,
+    )
+{% endif %}
+
+    # Preprocessing the datasets.
+    # First we tokenize all the texts.
+    if training_args.do_train:
+        column_names = datasets["train"].column_names
+    else:
+        column_names = datasets["validation"].column_names
+    text_column_name = "text" if "text" in column_names else column_names[0]
+
+    def tokenize_function(examples):
+        return tokenizer(examples[text_column_name], padding="max_length", truncation=True)
+
+    tokenized_datasets = datasets.map(
+        tokenize_function,
+        batched=True,
+        num_proc=data_args.preprocessing_num_workers,
+        remove_columns=[text_column_name],
+        load_from_cache_file=not data_args.overwrite_cache,
+    )
+
+    # Data collator
+    data_collator=default_data_collator
+
+    # Initialize our Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+    )
+
+    # Training
+    if training_args.do_train:
+        trainer.train(
+            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
+        )
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+    # Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        results = trainer.evaluate()
+
+        output_eval_file = os.path.join(training_args.output_dir, "eval_results_{{cookiecutter.example_shortcut}}.txt")
+        if trainer.is_world_process_zero():
+            with open(output_eval_file, "w") as writer:
+                logger.info("***** Eval results *****")
+                for key, value in results.items():
+                    logger.info(f"  {key} = {value}")
+                    writer.write(f"{key} = {value}\n")
+
+    return results
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()