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import os
from argparse import ArgumentParser, Namespace
from logging import getLogger
from transformers import SingleSentenceClassificationProcessor as Processor
from transformers import TextClassificationPipeline, is_tf_available, is_torch_available
from transformers.commands 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")
# TF training parameters
USE_XLA = False
USE_AMP = False
def train_command_factory(args: Namespace):
"""
Factory function used to instantiate serving server from provided command line arguments.
:return: ServeCommand
"""
return TrainCommand(args)
class TrainCommand(BaseTransformersCLICommand):
@staticmethod
def register_subcommand(parser: ArgumentParser):
"""
Register this command to argparse so it's available for the transformer-cli
:param parser: Root parser to register command-specific arguments
:return:
"""
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 = getLogger("transformers-cli/training")
self.framework = "tf" if is_tf_available() else "torch"
os.makedirs(args.output, exist_ok=True)
assert os.path.isdir(args.output)
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("Loading {} pipeline for {}".format(args.task, 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("Loading dataset from {}".format(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("Loading validation dataset from {}".format(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,
)
# Save trained pipeline
self.pipeline.save_pretrained(self.output)
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