BIG Reorganize examples (#4213)
* Created using Colaboratory * [examples] reorganize files * remove run_tpu_glue.py as superseded by TPU support in Trainer * Bugfix: int, not tuple * move files around
This commit is contained in:
Julien Chaumond
211
examples/text-classification/run_glue.py
Normal file
211
examples/text-classification/run_glue.py
Normal file
@@ -0,0 +1,211 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
|
||||
# Copyright (c) 2018, NVIDIA CORPORATION. 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 (Bert, XLM, XLNet, RoBERTa, Albert, XLM-RoBERTa)."""
|
||||
|
||||
|
||||
import dataclasses
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, Optional
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, EvalPrediction, GlueDataset
|
||||
from transformers import GlueDataTrainingArguments as DataTrainingArguments
|
||||
from transformers import (
|
||||
HfArgumentParser,
|
||||
Trainer,
|
||||
TrainingArguments,
|
||||
glue_compute_metrics,
|
||||
glue_output_modes,
|
||||
glue_tasks_num_labels,
|
||||
set_seed,
|
||||
)
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelArguments:
|
||||
"""
|
||||
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
|
||||
"""
|
||||
|
||||
model_name_or_path: str = field(
|
||||
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
|
||||
)
|
||||
config_name: Optional[str] = field(
|
||||
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
||||
)
|
||||
tokenizer_name: Optional[str] = field(
|
||||
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
||||
)
|
||||
cache_dir: Optional[str] = field(
|
||||
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
|
||||
)
|
||||
|
||||
|
||||
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 training_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",
|
||||
training_args.local_rank,
|
||||
training_args.device,
|
||||
training_args.n_gpu,
|
||||
bool(training_args.local_rank != -1),
|
||||
training_args.fp16,
|
||||
)
|
||||
logger.info("Training/evaluation parameters %s", training_args)
|
||||
|
||||
# Set seed
|
||||
set_seed(training_args.seed)
|
||||
|
||||
try:
|
||||
num_labels = glue_tasks_num_labels[data_args.task_name]
|
||||
output_mode = glue_output_modes[data_args.task_name]
|
||||
except KeyError:
|
||||
raise ValueError("Task not found: %s" % (data_args.task_name))
|
||||
|
||||
# Load pretrained model and tokenizer
|
||||
#
|
||||
# Distributed training:
|
||||
# The .from_pretrained methods guarantee that only one local process can concurrently
|
||||
# download model & vocab.
|
||||
|
||||
config = AutoConfig.from_pretrained(
|
||||
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
|
||||
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,
|
||||
)
|
||||
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,
|
||||
)
|
||||
|
||||
# Get datasets
|
||||
train_dataset = (
|
||||
GlueDataset(data_args, tokenizer=tokenizer, local_rank=training_args.local_rank)
|
||||
if training_args.do_train
|
||||
else None
|
||||
)
|
||||
eval_dataset = (
|
||||
GlueDataset(data_args, tokenizer=tokenizer, local_rank=training_args.local_rank, evaluate=True)
|
||||
if training_args.do_eval
|
||||
else None
|
||||
)
|
||||
|
||||
def compute_metrics(p: EvalPrediction) -> Dict:
|
||||
if output_mode == "classification":
|
||||
preds = np.argmax(p.predictions, axis=1)
|
||||
elif output_mode == "regression":
|
||||
preds = np.squeeze(p.predictions)
|
||||
return glue_compute_metrics(data_args.task_name, preds, p.label_ids)
|
||||
|
||||
# Initialize our Trainer
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
train_dataset=train_dataset,
|
||||
eval_dataset=eval_dataset,
|
||||
compute_metrics=compute_metrics,
|
||||
)
|
||||
|
||||
# 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()
|
||||
# For convenience, we also re-save the tokenizer to the same directory,
|
||||
# so that you can share your model easily on huggingface.co/models =)
|
||||
if trainer.is_world_master():
|
||||
tokenizer.save_pretrained(training_args.output_dir)
|
||||
|
||||
# Evaluation
|
||||
results = {}
|
||||
if training_args.do_eval and training_args.local_rank in [-1, 0]:
|
||||
logger.info("*** Evaluate ***")
|
||||
|
||||
# Loop to handle MNLI double evaluation (matched, mis-matched)
|
||||
eval_datasets = [eval_dataset]
|
||||
if data_args.task_name == "mnli":
|
||||
mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
|
||||
eval_datasets.append(
|
||||
GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, local_rank=training_args.local_rank, evaluate=True)
|
||||
)
|
||||
|
||||
for eval_dataset in eval_datasets:
|
||||
result = trainer.evaluate(eval_dataset=eval_dataset)
|
||||
|
||||
output_eval_file = os.path.join(
|
||||
training_args.output_dir, f"eval_results_{eval_dataset.args.task_name}.txt"
|
||||
)
|
||||
with open(output_eval_file, "w") as writer:
|
||||
logger.info("***** Eval results {} *****".format(eval_dataset.args.task_name))
|
||||
for key, value in result.items():
|
||||
logger.info(" %s = %s", key, value)
|
||||
writer.write("%s = %s\n" % (key, value))
|
||||
|
||||
results.update(result)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
def _mp_fn(index):
|
||||
# For xla_spawn (TPUs)
|
||||
main()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user