2020 the huggingface team 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 orglicenseslicense2 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 tf training parameters factory function used to instantiate training command from provided command line arguments returns traincommand register this command to argparse so it s available for the transformercli args parser root parser to register commandspecific arguments save trained pipeline 2020 the huggingface team 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 tf training parameters factory function used to instantiate training command from provided command line arguments returns traincommand register this command to argparse so it s available for the transformer cli args parser root parser to register command specific arguments save trained pipeline
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")


USE_XLA = False
USE_AMP = False


def train_command_factory(args: Namespace):
    
    return TrainCommand(args)


class TrainCommand(BaseTransformersCLICommand):
    @staticmethod
    def register_subcommand(parser: ArgumentParser):
        
        train_parser = parser.add_parser("train", help="CLI tool to train a model on a task.")

        train_parser.add_argument(
            "--train_data",
            type=str,
            required=True,
            help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.",
        )
        train_parser.add_argument(
            "--column_label", type=int, default=0, help="Column of the dataset csv file with example labels."
        )
        train_parser.add_argument(
            "--column_text", type=int, default=1, help="Column of the dataset csv file with example texts."
        )
        train_parser.add_argument(
            "--column_id", type=int, default=2, help="Column of the dataset csv file with example ids."
        )
        train_parser.add_argument(
            "--skip_first_row", action="store_true", help="Skip the first row of the csv file (headers)."
        )

        train_parser.add_argument("--validation_data", type=str, default="", help="path to validation dataset.")
        train_parser.add_argument(
            "--validation_split",
            type=float,
            default=0.1,
            help="if validation dataset is not provided, fraction of train dataset to use as validation dataset.",
        )

        train_parser.add_argument("--output", type=str, default="./", help="path to saved the trained model.")

        train_parser.add_argument(
            "--task", type=str, default="text_classification", help="Task to train the model on."
        )
        train_parser.add_argument(
            "--model", type=str, default="bert-base-uncased", help="Model's name or path to stored model."
        )
        train_parser.add_argument("--train_batch_size", type=int, default=32, help="Batch size for training.")
        train_parser.add_argument("--valid_batch_size", type=int, default=64, help="Batch size for validation.")
        train_parser.add_argument("--learning_rate", type=float, default=3e-5, help="Learning rate.")
        train_parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon for Adam optimizer.")
        train_parser.set_defaults(func=train_command_factory)

    def __init__(self, args: Namespace):
        self.logger = logging.get_logger("transformers-cli/training")

        self.framework = "tf" if is_tf_available() else "torch"

        os.makedirs(args.output, exist_ok=True)
        self.output = args.output

        self.column_label = args.column_label
        self.column_text = args.column_text
        self.column_id = args.column_id

        self.logger.info(f"Loading {args.task} pipeline for {args.model}")
        if args.task == "text_classification":
            self.pipeline = TextClassificationPipeline.from_pretrained(args.model)
        elif args.task == "token_classification":
            raise NotImplementedError
        elif args.task == "question_answering":
            raise NotImplementedError

        self.logger.info(f"Loading dataset from {args.train_data}")
        self.train_dataset = Processor.create_from_csv(
            args.train_data,
            column_label=args.column_label,
            column_text=args.column_text,
            column_id=args.column_id,
            skip_first_row=args.skip_first_row,
        )
        self.valid_dataset = None
        if args.validation_data:
            self.logger.info(f"Loading validation dataset from {args.validation_data}")
            self.valid_dataset = Processor.create_from_csv(
                args.validation_data,
                column_label=args.column_label,
                column_text=args.column_text,
                column_id=args.column_id,
                skip_first_row=args.skip_first_row,
            )

        self.validation_split = args.validation_split
        self.train_batch_size = args.train_batch_size
        self.valid_batch_size = args.valid_batch_size
        self.learning_rate = args.learning_rate
        self.adam_epsilon = args.adam_epsilon

    def run(self):
        if self.framework == "tf":
            return self.run_tf()
        return self.run_torch()

    def run_torch(self):
        raise NotImplementedError

    def run_tf(self):
        self.pipeline.fit(
            self.train_dataset,
            validation_data=self.valid_dataset,
            validation_split=self.validation_split,
            learning_rate=self.learning_rate,
            adam_epsilon=self.adam_epsilon,
            train_batch_size=self.train_batch_size,
            valid_batch_size=self.valid_batch_size,
        )

        
        self.pipeline.save_pretrained(self.output)