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Examples reorg (#11350)

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* Base move

* Examples reorganization

* Update references

* Put back test data

* Move conftest

* More fixes

* Move test data to test fixtures

* Update path

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Address review comments and clean

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Sylvain Gugger
2021-04-21 11:11:20 -04:00
committed by GitHub
parent ca7ff64f5b
commit dabeb15292
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examples/tensorflow/question-answering/README.md Normal file
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<!---
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.
-->
## SQuAD with the Tensorflow Trainer
```bash
python run_tf_squad.py \
--model_name_or_path bert-base-uncased \
--output_dir model \
--max_seq_length 384 \
--num_train_epochs 2 \
--per_gpu_train_batch_size 8 \
--per_gpu_eval_batch_size 16 \
--do_train \
--logging_dir logs \
--logging_steps 10 \
--learning_rate 3e-5 \
--doc_stride 128
```
For the moment evaluation is not available in the Tensorflow Trainer only the training.

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examples/tensorflow/question-answering/requirements.txt Normal file
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datasets >= 1.4.0
tensorflow >= 2.3.0

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examples/tensorflow/question-answering/run_tf_squad.py Executable file
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#!/usr/bin/env python
# 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.
""" Fine-tuning the library models for question-answering."""
import logging
import os
from dataclasses import dataclass, field
from typing import Optional
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
TFAutoModelForQuestionAnswering,
TFTrainer,
TFTrainingArguments,
squad_convert_examples_to_features,
)
from transformers.data.processors.squad import SquadV1Processor, SquadV2Processor
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
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"}
)
use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
data_dir: Optional[str] = field(
default=None, metadata={"help": "The input data dir. Should contain the .json files for the SQuAD task."}
)
use_tfds: Optional[bool] = field(default=True, metadata={"help": "If TFDS should be used or not."})
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."
},
)
doc_stride: int = field(
default=128,
metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
)
max_query_length: int = field(
default=64,
metadata={
"help": "The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length."
},
)
max_answer_length: int = field(
default=30,
metadata={
"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."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
version_2_with_negative: bool = field(
default=False, metadata={"help": "If true, the SQuAD examples contain some that do not have an answer."}
)
null_score_diff_threshold: float = field(
default=0.0, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
)
n_best_size: int = field(
default=20, metadata={"help": "If null_score - best_non_null is greater than the threshold predict null."}
)
lang_id: int = field(
default=0,
metadata={
"help": "language id of input for language-specific xlm models (see tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
},
)
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, TFTrainingArguments))
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,
)
logger.info(
f"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, "
f"16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Prepare Question-Answering task
# 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,
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,
)
with training_args.strategy.scope():
model = TFAutoModelForQuestionAnswering.from_pretrained(
model_args.model_name_or_path,
from_pt=bool(".bin" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
# Get datasets
if data_args.use_tfds:
if data_args.version_2_with_negative:
logger.warning("tensorflow_datasets does not handle version 2 of SQuAD. Switch to version 1 automatically")
try:
import tensorflow_datasets as tfds
except ImportError:
raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")
tfds_examples = tfds.load("squad", data_dir=data_args.data_dir)
train_examples = (
SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=False)
if training_args.do_train
else None
)
eval_examples = (
SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=True)
if training_args.do_eval
else None
)
else:
processor = SquadV2Processor() if data_args.version_2_with_negative else SquadV1Processor()
train_examples = processor.get_train_examples(data_args.data_dir) if training_args.do_train else None
eval_examples = processor.get_dev_examples(data_args.data_dir) if training_args.do_eval else None
train_dataset = (
squad_convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
doc_stride=data_args.doc_stride,
max_query_length=data_args.max_query_length,
is_training=True,
return_dataset="tf",
)
if training_args.do_train
else None
)
train_dataset = train_dataset.apply(tf.data.experimental.assert_cardinality(len(train_examples)))
eval_dataset = (
squad_convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
doc_stride=data_args.doc_stride,
max_query_length=data_args.max_query_length,
is_training=False,
return_dataset="tf",
)
if training_args.do_eval
else None
)
eval_dataset = eval_dataset.apply(tf.data.experimental.assert_cardinality(len(eval_examples)))
# Initialize our Trainer
trainer = TFTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
)
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir)
if __name__ == "__main__":
main()