roberta_year_as_token_better_finetunning

roberta_year_as_token_better_finetunning/01_create_datasets.py

@@ -0,0 +1,34 @@
+import datetime
+from config import LABELS_DICT
+
+with open('../test-A/in.tsv','r') as f_in, open(f'./test-A_huggingface_format_year.csv', 'w') as f_hf:
+    f_hf.write('text\tyear_cont\tdate\tday_of_year\tday_of_month\tmonth\tyear\tweekday\tlabel\n')
+    for line_in in f_in:
+        year_cont, date, text = line_in.rstrip('\n').split('\t')
+        d = datetime.datetime.strptime(date,"%Y%m%d")
+        day_of_year = str(d.timetuple().tm_yday)
+        day_of_month = str(d.day)
+        month = str(d.month)
+        year = str(d.year)
+        weekday = str(d.weekday())
+        day_of_year = str(d.timetuple().tm_yday)
+        text = '<pad> ' *4 + text
+        f_hf.write(text +'\t' +year_cont +'\t'+ date + '\t' +  day_of_year + '\t' + day_of_month + '\t' + month + '\t' + year + '\t' + weekday + '\t' +  str('0') + '\n')
+
+
+for dataset in 'train', 'dev-0':
+    with open(f'../{dataset}/in.tsv') as f_in, open(f'../{dataset}/expected.tsv') as f_exp, open(f'./{dataset}_huggingface_format_year.csv','w') as f_hf:
+        f_hf.write('text\tyear_cont\tdate\tday_of_year\tday_of_month\tmonth\tyear\tweekday\tlabel\n')
+        for line_in, line_exp in zip(f_in, f_exp):
+            label = str(LABELS_DICT[line_exp.rstrip('\n')])
+            year_cont,date,text = line_in.rstrip('\n').split('\t')
+            d = datetime.datetime.strptime(date,"%Y%m%d")
+            day_of_year = str(d.timetuple().tm_yday)
+            day_of_month = str(d.day)
+            month = str(d.month)
+            year = str(d.year)
+            weekday = str(d.weekday())
+            day_of_year = str(d.timetuple().tm_yday)
+            text = '<pad> ' *4 + text
+            f_hf.write(text +'\t' +year_cont +'\t'+ date + '\t'+ day_of_year + '\t' + day_of_month + '\t' + month + '\t' + year + '\t' + weekday + '\t'  +  label + '\n')
+

roberta_year_as_token_better_finetunning/02_load_dataset.py

@@ -0,0 +1,56 @@
+import pickle
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from config import MODEL
+from tqdm import tqdm
+
+dataset = load_dataset('csv', sep='\t', data_files={'train': ['./train_huggingface_format_year.csv'], 'test': ['./dev-0_huggingface_format_year.csv']})
+test_dataset = load_dataset('csv', sep='\t', data_files='./test-A_huggingface_format_year.csv')
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+
+def tokenize_function(examples):
+    t = tokenizer(examples["text"], padding="max_length", truncation=True)
+    examples['year'] = [x - 1995 for x in examples['year']]
+    for column in 'date', 'day_of_month', 'day_of_year', 'month', 'year', 'weekday', 'year_cont':
+        #t[column] = [[a] * b.index(1) + [0] *(len(b) - b.index(1)) for a,b in zip(examples[column], t['input_ids'])]
+        t[column] = [[0] * len(i) for i in t.input_ids]
+    for i in range(len(t['input_ids'])):
+        t['year'][i][1] = examples['year'][i]
+        t['month'][i][2] = examples['month'][i]
+        t['day_of_month'][i][3] = examples['day_of_month'][i]
+        t['weekday'][i][4] = examples['weekday'][i]
+    return t
+
+test_tokenized_datasets = test_dataset.map(tokenize_function, batched=True)
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+
+#for d in ('train', 'test'):
+#        for i in tqdm(range(len(tokenized_datasets[d]))):
+#            tokenized_datasets[d][i][column] = [tokenized_datasets[d][i][column] ] * 512 #len(tokenized_datasets[d][i]['input_ids'])
+#
+#d = 'train'
+#for column in tqdm(('date', 'day_of_month', 'day_of_year', 'month', 'year', 'year_cont')):
+#    for i in tqdm(range(len(test_tokenized_datasets[d]))):
+#        test_tokenized_datasets[d][i][column] = [test_tokenized_datasets[d][i][column] ] * 512 #len(test_tokenized_datasets[d][i]['input_ids'])
+
+train_dataset = tokenized_datasets["train"].shuffle(seed=42)
+eval_dataset_full = tokenized_datasets["test"]
+eval_dataset_small = tokenized_datasets["test"].select(range(2000))
+test_dataset = test_tokenized_datasets["train"]
+
+
+with open('train_dataset.pickle','wb') as f_p:
+    pickle.dump(train_dataset, f_p)
+
+with open('eval_dataset_small.pickle','wb') as f_p:
+    pickle.dump(eval_dataset_small, f_p)
+
+with open('eval_dataset_full.pickle','wb') as f_p:
+    pickle.dump(eval_dataset_full, f_p)
+
+with open('test_dataset.pickle','wb') as f_p:
+    pickle.dump(test_dataset, f_p)
+     
+

roberta_year_as_token_better_finetunning/04_predict.py

@@ -0,0 +1,85 @@
+import pickle
+from config import LABELS_LIST, MODEL
+
+with open('train_dataset.pickle','rb') as f_p:
+    train_dataset = pickle.load(f_p)
+
+with open('eval_dataset_small.pickle','rb') as f_p:
+    eval_dataset_small = pickle.load(f_p)
+
+with open('eval_dataset_full.pickle','rb') as f_p:
+    eval_dataset_full = pickle.load(f_p)
+
+with open('test_dataset.pickle','rb') as f_p:
+    test_dataset = pickle.load(f_p)
+
+
+from transformers import AutoModelForSequenceClassification
+
+model = AutoModelForSequenceClassification.from_pretrained('roberta-ireland').cuda()
+
+from transformers import TrainingArguments
+
+
+training_args = TrainingArguments("test_trainer",
+        per_device_train_batch_size=4,
+        per_device_eval_batch_size=4,
+        evaluation_strategy='steps',
+        #eval_steps=2_000,
+        #save_steps=2_000,
+        eval_steps=2_000,
+        save_steps=20_000,
+        num_train_epochs=1,
+        gradient_accumulation_steps=2,
+        learning_rate = 1e-6,
+        #warmup_steps=4_000,
+        warmup_steps=4,
+        load_best_model_at_end=True,
+        )
+
+import numpy as np
+from datasets import load_metric
+
+metric = load_metric("accuracy")
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    predictions = np.argmax(logits, axis=-1)
+    return metric.compute(predictions=predictions, references=labels)
+
+
+from transformers import Trainer
+
+trainer = Trainer(
+            model=model,
+            args=training_args,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset_small,
+            compute_metrics=compute_metrics,
+            )
+
+
+eval_predictions = trainer.predict(eval_dataset_full).predictions.argmax(1)
+
+with open('../dev-0/out.tsv', 'w') as f_out:
+    for pred in eval_predictions:
+        f_out.write(LABELS_LIST[pred] + '\n')
+
+test_predictions = trainer.predict(test_dataset).predictions.argmax(1)
+with open('../test-A/out.tsv', 'w') as f_out:
+    for pred in test_predictions:
+        f_out.write(LABELS_LIST[pred] + '\n')
+
+#model = AutoModelForSequenceClassification.from_pretrained('roberta-retrained/')
+
+#for dataset in ('dev-0', 'test-A'):
+#    with open(f'../{dataset}/in.tsv') as f_in, open(f'../{dataset}/out.tsv','w') as f_out:
+#        for line_in in tqdm(f_in, total=150_000):
+#            _,_, text = line_in.split('\t')
+#            text = text.rstrip('\n')
+#            inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt").to(device)
+#            outputs = model(**inputs)
+#            probs = outputs[0].softmax(1)
+#            prediction = LABELS_LIST[probs.argmax(1)]
+#            f_out.write(prediction + '\n')
+#

roberta_year_as_token_better_finetunning/config.py

@@ -0,0 +1,8 @@
+LABELS_DICT = {'positive':0,
+        'negative':1}
+
+
+LABELS_LIST = ['positive',
+        'negative']
+
+MODEL = 'roberta-base'

roberta_year_as_token_better_finetunning/run.sh

@@ -0,0 +1,2 @@
+python run_glue.py   --model_name_or_path roberta-base   --train_file train_huggingface_format_year.csv --validation_file dev-0_huggingface_format_year.csv     --do_train --do_eval  --max_seq_length 64   --per_device_train_batch_size 32   --learning_rate 2e-5   --num_train_epochs 3 --save_steps=5000 --eval_steps=5000 --evaluation_strategy steps   --output_dir ./roberta-ireland
+

roberta_year_as_token_better_finetunning/run_glue.py

@@ -0,0 +1,578 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Finetuning the library models for sequence classification on GLUE."""
+# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
+
+import logging
+import os
+import random
+import sys
+from dataclasses import dataclass, field
+from typing import Optional
+
+import datasets
+import numpy as np
+from datasets import load_dataset, load_metric
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    EvalPrediction,
+    HfArgumentParser,
+    PretrainedConfig,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.11.0.dev0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
+
+task_to_keys = {
+    "cola": ("sentence", None),
+    "mnli": ("premise", "hypothesis"),
+    "mrpc": ("sentence1", "sentence2"),
+    "qnli": ("question", "sentence"),
+    "qqp": ("question1", "question2"),
+    "rte": ("sentence1", "sentence2"),
+    "sst2": ("sentence", None),
+    "stsb": ("sentence1", "sentence2"),
+    "wnli": ("sentence1", "sentence2"),
+}
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    task_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
+    )
+    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)."}
+    )
+    max_seq_length: int = field(
+        default=128,
+        metadata={
+            "help": "The maximum total input sequence length after tokenization. Sequences longer "
+            "than this will be truncated, sequences shorter will be padded."
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
+    )
+    pad_to_max_length: bool = field(
+        default=True,
+        metadata={
+            "help": "Whether to pad all samples to `max_seq_length`. "
+            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+            "value if set."
+        },
+    )
+    max_predict_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
+            "value if set."
+        },
+    )
+    train_file: Optional[str] = field(
+        default=None, metadata={"help": "A csv or a json file containing the training data."}
+    )
+    validation_file: Optional[str] = field(
+        default=None, metadata={"help": "A csv or a json file containing the validation data."}
+    )
+    test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})
+
+    def __post_init__(self):
+        if self.task_name is not None:
+            self.task_name = self.task_name.lower()
+            if self.task_name not in task_to_keys.keys():
+                raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
+        elif self.dataset_name is not None:
+            pass
+        elif self.train_file is None or self.validation_file is None:
+            raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.")
+        else:
+            train_extension = self.train_file.split(".")[-1]
+            assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+            validation_extension = self.validation_file.split(".")[-1]
+            assert (
+                validation_extension == train_extension
+            ), "`validation_file` should have the same extension (csv or json) as `train_file`."
+
+
+@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 huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+            "with private models)."
+        },
+    )
+
+
+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()
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # 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}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
+    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
+    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
+    # label if at least two columns are provided.
+    #
+    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
+    # single 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.task_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset("glue", data_args.task_name, cache_dir=model_args.cache_dir)
+    elif data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
+        )
+    else:
+        # Loading a dataset from your local files.
+        # CSV/JSON training and evaluation files are needed.
+        data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
+        #data_files = {"train": data_args.train_file, "validation": data_args.validation_file, "test":data_args.test_file}
+
+        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
+        # when you use `do_predict` without specifying a GLUE benchmark task.
+        if training_args.do_predict:
+            if data_args.test_file is not None:
+                train_extension = data_args.train_file.split(".")[-1]
+                test_extension = data_args.test_file.split(".")[-1]
+                assert (
+                    test_extension == train_extension
+                ), "`test_file` should have the same extension (csv or json) as `train_file`."
+                data_files["test"] = data_args.test_file
+            else:
+                raise ValueError("Need either a GLUE task or a test file for `do_predict`.")
+
+        for key in data_files.keys():
+            logger.info(f"load a local file for {key}: {data_files[key]}")
+
+        if data_args.train_file.endswith(".csv"):
+            # Loading a dataset from local csv files
+            raw_datasets = load_dataset("csv", data_files=data_files, cache_dir=model_args.cache_dir, sep='\t')
+        else:
+            # Loading a dataset from local json files
+            raw_datasets = load_dataset("json", data_files=data_files, cache_dir=model_args.cache_dir)
+    # See more about loading any type of standard or custom dataset at
+    # https://huggingface.co/docs/datasets/loading_datasets.html.
+
+    # Labels
+    if data_args.task_name is not None:
+        is_regression = data_args.task_name == "stsb"
+        if not is_regression:
+            label_list = raw_datasets["train"].features["label"].names
+            num_labels = len(label_list)
+        else:
+            num_labels = 1
+    else:
+        # Trying to have good defaults here, don't hesitate to tweak to your needs.
+        is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
+        if is_regression:
+            num_labels = 1
+        else:
+            # A useful fast method:
+            # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
+            label_list = raw_datasets["train"].unique("label")
+            label_list.sort()  # Let's sort it for determinism
+            num_labels = len(label_list)
+
+    # Load pretrained model and tokenizer
+    #
+    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        num_labels=num_labels,
+        finetuning_task=data_args.task_name,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    model = 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,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    # Preprocessing the raw_datasets
+    if data_args.task_name is not None:
+        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
+    else:
+        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
+        non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
+        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
+            sentence1_key, sentence2_key = "sentence1", "sentence2"
+        else:
+            if len(non_label_column_names) >= 2:
+                sentence1_key, sentence2_key = non_label_column_names[:2]
+            else:
+                sentence1_key, sentence2_key = non_label_column_names[0], None
+
+    # Padding strategy
+    if data_args.pad_to_max_length:
+        padding = "max_length"
+    else:
+        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
+        padding = False
+
+    # Some models have set the order of the labels to use, so let's make sure we do use it.
+    label_to_id = None
+    if (
+        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
+        and data_args.task_name is not None
+        and not is_regression
+    ):
+        # Some have all caps in their config, some don't.
+        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
+        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
+            label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)}
+        else:
+            logger.warning(
+                "Your model seems to have been trained with labels, but they don't match the dataset: ",
+                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
+                "\nIgnoring the model labels as a result.",
+            )
+    elif data_args.task_name is None and not is_regression:
+        label_to_id = {v: i for i, v in enumerate(label_list)}
+
+    if label_to_id is not None:
+        model.config.label2id = label_to_id
+        model.config.id2label = {id: label for label, id in config.label2id.items()}
+    elif data_args.task_name is not None and not is_regression:
+        model.config.label2id = {l: i for i, l in enumerate(label_list)}
+        model.config.id2label = {id: label for label, id in config.label2id.items()}
+
+    if data_args.max_seq_length > tokenizer.model_max_length:
+        logger.warning(
+            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
+            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+        )
+    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+
+    def preprocess_function(examples):
+        # Tokenize the texts
+        args = (
+            (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
+        )
+        result = tokenizer(examples['text'], padding=padding, max_length=max_seq_length, truncation=True)
+
+        # Map labels to IDs (not necessary for GLUE tasks)
+        if label_to_id is not None and "label" in examples:
+            result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
+
+        t = result
+
+        for column in 'date', 'day_of_month', 'day_of_year', 'month', 'year', 'weekday', 'year_cont':
+            t[column] = [[0] * len(i) for i in t.input_ids]
+        for i in range(len(t['input_ids'])):
+            t['year'][i][1] = examples['year'][i] - 1995
+            t['month'][i][2] = examples['month'][i]
+            t['day_of_month'][i][3] = examples['day_of_month'][i]
+            t['weekday'][i][4] = examples['weekday'][i]
+
+        return t
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        raw_datasets = raw_datasets.map(
+            preprocess_function,
+            batched=True,
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on dataset",
+        )
+    if training_args.do_train:
+        if "train" not in raw_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = raw_datasets["train"]
+        if data_args.max_train_samples is not None:
+            train_dataset = train_dataset.select(range(data_args.max_train_samples))
+
+    if training_args.do_eval:
+        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
+        if data_args.max_eval_samples is not None:
+            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
+
+    if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
+        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
+            raise ValueError("--do_predict requires a test dataset")
+        predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"]
+        if data_args.max_predict_samples is not None:
+            predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
+
+    # Log a few random samples from the training set:
+    if training_args.do_train:
+        for index in random.sample(range(len(train_dataset)), 3):
+            logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+
+    # Get the metric function
+    if data_args.task_name is not None:
+        metric = load_metric("glue", data_args.task_name)
+    else:
+        metric = load_metric("accuracy")
+
+    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
+    # predictions and label_ids field) and has to return a dictionary string to float.
+    def compute_metrics(p: EvalPrediction):
+        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
+        preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
+        if data_args.task_name is not None:
+            result = metric.compute(predictions=preds, references=p.label_ids)
+            if len(result) > 1:
+                result["combined_score"] = np.mean(list(result.values())).item()
+            return result
+        elif is_regression:
+            return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
+        else:
+            return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}
+
+    # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
+    if data_args.pad_to_max_length:
+        data_collator = default_data_collator
+    elif training_args.fp16:
+        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
+    else:
+        data_collator = None
+
+    # Initialize our Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        compute_metrics=compute_metrics,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        # Loop to handle MNLI double evaluation (matched, mis-matched)
+        tasks = [data_args.task_name]
+        eval_datasets = [eval_dataset]
+        if data_args.task_name == "mnli":
+            tasks.append("mnli-mm")
+            eval_datasets.append(raw_datasets["validation_mismatched"])
+
+        for eval_dataset, task in zip(eval_datasets, tasks):
+            metrics = trainer.evaluate(eval_dataset=eval_dataset)
+
+            max_eval_samples = (
+                data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+            )
+            metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+
+            trainer.log_metrics("eval", metrics)
+            trainer.save_metrics("eval", metrics)
+
+    if training_args.do_predict:
+        logger.info("*** Predict ***")
+
+        # Loop to handle MNLI double evaluation (matched, mis-matched)
+        tasks = [data_args.task_name]
+        predict_datasets = [predict_dataset]
+        if data_args.task_name == "mnli":
+            tasks.append("mnli-mm")
+            predict_datasets.append(raw_datasets["test_mismatched"])
+
+        for predict_dataset, task in zip(predict_datasets, tasks):
+            # Removing the `label` columns because it contains -1 and Trainer won't like that.
+            predict_dataset = predict_dataset.remove_columns("label")
+            predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
+            predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
+
+            output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt")
+            if trainer.is_world_process_zero():
+                with open(output_predict_file, "w") as writer:
+                    logger.info(f"***** Predict results {task} *****")
+                    writer.write("index\tprediction\n")
+                    for index, item in enumerate(predictions):
+                        if is_regression:
+                            writer.write(f"{index}\t{item:3.3f}\n")
+                        else:
+                            item = label_list[item]
+                            writer.write(f"{index}\t{item}\n")
+
+    if training_args.push_to_hub:
+        kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
+        if data_args.task_name is not None:
+            kwargs["language"] = "en"
+            kwargs["dataset_tags"] = "glue"
+            kwargs["dataset_args"] = data_args.task_name
+            kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
+
+        trainer.push_to_hub(**kwargs)
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()