847 lines
38 KiB
Python
Executable File
847 lines
38 KiB
Python
Executable File
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2020 The HuggingFace Team All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Fine-tuning the library models for question answering.
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"""
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# You can also adapt this script on your own question answering task. Pointers for this are left as comments.
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import json
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import logging
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import os
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import sys
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import warnings
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional
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import evaluate
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import tensorflow as tf
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from datasets import load_dataset
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from packaging.version import parse
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from utils_qa import postprocess_qa_predictions
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import transformers
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from transformers import (
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AutoConfig,
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AutoTokenizer,
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EvalPrediction,
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HfArgumentParser,
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PreTrainedTokenizerFast,
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PushToHubCallback,
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TFAutoModelForQuestionAnswering,
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TFTrainingArguments,
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create_optimizer,
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set_seed,
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)
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from transformers.utils import CONFIG_NAME, TF2_WEIGHTS_NAME, check_min_version, send_example_telemetry
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try:
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import tf_keras as keras
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except (ModuleNotFoundError, ImportError):
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import keras
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if parse(keras.__version__).major > 2:
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raise ValueError(
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"Your currently installed version of Keras is Keras 3, but this is not yet supported in "
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"Transformers. Please install the backwards-compatible tf-keras package with "
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"`pip install tf-keras`."
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)
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.41.0.dev0")
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logger = logging.getLogger(__name__)
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# region Arguments
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Path to directory to store the pretrained models downloaded from huggingface.co"},
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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token: str = field(
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default=None,
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metadata={
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"help": (
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"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
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"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
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)
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},
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)
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use_auth_token: bool = field(
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default=None,
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metadata={
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"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
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},
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)
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trust_remote_code: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
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"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
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"execute code present on the Hub on your local machine."
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)
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},
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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dataset_name: Optional[str] = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
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validation_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
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)
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test_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input test data file to evaluate the perplexity on (a text file)."},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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max_seq_length: int = field(
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default=384,
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metadata={
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"help": (
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"The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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)
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},
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)
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pad_to_max_length: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether to pad all samples to `max_seq_length`. If False, will pad the samples dynamically when"
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" batching to the maximum length in the batch (which can be faster on GPU but will be slower on TPU)."
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)
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},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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)
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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)
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},
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)
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max_predict_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of prediction examples to this "
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"value if set."
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)
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},
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)
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version_2_with_negative: bool = field(
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default=False, metadata={"help": "If true, some of the examples do not have an answer."}
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)
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null_score_diff_threshold: float = field(
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default=0.0,
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metadata={
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"help": (
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"The threshold used to select the null answer: if the best answer has a score that is less than "
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"the score of the null answer minus this threshold, the null answer is selected for this example. "
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"Only useful when `version_2_with_negative=True`."
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)
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},
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)
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doc_stride: int = field(
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default=128,
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metadata={"help": "When splitting up a long document into chunks, how much stride to take between chunks."},
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)
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n_best_size: int = field(
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default=20,
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metadata={"help": "The total number of n-best predictions to generate when looking for an answer."},
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)
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max_answer_length: int = field(
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default=30,
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metadata={
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"help": (
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"The maximum length of an answer that can be generated. This is needed because the start "
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"and end predictions are not conditioned on one another."
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)
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},
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)
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def __post_init__(self):
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if (
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self.dataset_name is None
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and self.train_file is None
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and self.validation_file is None
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and self.test_file is None
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):
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raise ValueError("Need either a dataset name or a training/validation file/test_file.")
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else:
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if self.train_file is not None:
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extension = self.train_file.split(".")[-1]
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assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
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if self.test_file is not None:
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extension = self.test_file.split(".")[-1]
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assert extension in ["csv", "json"], "`test_file` should be a csv or a json file."
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# endregion
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# region Helper classes
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class SavePretrainedCallback(keras.callbacks.Callback):
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# Hugging Face models have a save_pretrained() method that saves both the weights and the necessary
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# metadata to allow them to be loaded as a pretrained model in future. This is a simple Keras callback
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# that saves the model with this method after each epoch.
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def __init__(self, output_dir, **kwargs):
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super().__init__()
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self.output_dir = output_dir
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def on_epoch_end(self, epoch, logs=None):
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self.model.save_pretrained(self.output_dir)
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# endregion
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def main():
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# region Argument parsing
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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if model_args.use_auth_token is not None:
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warnings.warn(
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"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
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FutureWarning,
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)
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if model_args.token is not None:
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raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
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model_args.token = model_args.use_auth_token
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# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
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# information sent is the one passed as arguments along with your Python/PyTorch versions.
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send_example_telemetry("run_qa", model_args, data_args, framework="tensorflow")
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output_dir = Path(training_args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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# endregion
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# region Checkpoints
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checkpoint = None
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if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir:
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if (output_dir / CONFIG_NAME).is_file() and (output_dir / TF2_WEIGHTS_NAME).is_file():
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checkpoint = output_dir
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logger.info(
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f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this"
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" behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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else:
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. "
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"Use --overwrite_output_dir to continue regardless."
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)
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# endregion
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# region Logging
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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handlers=[logging.StreamHandler(sys.stdout)],
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)
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logger.setLevel(logging.INFO if training_args.should_log else logging.WARN)
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# Set the verbosity to info of the Transformers logger (on main process only):
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if training_args.should_log:
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transformers.utils.logging.set_verbosity_info()
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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logger.info(f"Training/evaluation parameters {training_args}")
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# endregion
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# Set seed before initializing model.
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set_seed(training_args.seed)
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# region Load Data
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# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
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# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
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# (the dataset will be downloaded automatically from the datasets Hub).
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#
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# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
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# 'text' is found. You can easily tweak this behavior (see below).
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#
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# In distributed training, the load_dataset function guarantee that only one local process can concurrently
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# download the dataset.
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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datasets = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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else:
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data_files = {}
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if data_args.train_file is not None:
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data_files["train"] = data_args.train_file
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extension = data_args.train_file.split(".")[-1]
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if data_args.validation_file is not None:
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data_files["validation"] = data_args.validation_file
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extension = data_args.validation_file.split(".")[-1]
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if data_args.test_file is not None:
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data_files["test"] = data_args.test_file
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extension = data_args.test_file.split(".")[-1]
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datasets = load_dataset(
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extension,
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data_files=data_files,
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field="data",
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.
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# endregion
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# region Load pretrained model and tokenizer
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#
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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config = AutoConfig.from_pretrained(
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model_args.config_name if model_args.config_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_fast=True,
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revision=model_args.model_revision,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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# endregion
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# region Tokenizer check: this script requires a fast tokenizer.
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if not isinstance(tokenizer, PreTrainedTokenizerFast):
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raise ValueError(
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"This example script only works for models that have a fast tokenizer. Checkout the big table of models at"
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" https://huggingface.co/transformers/index.html#supported-frameworks to find the model types that meet"
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" this requirement"
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)
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# endregion
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# region Preprocessing the datasets
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# Preprocessing is slightly different for training and evaluation.
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if training_args.do_train:
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column_names = datasets["train"].column_names
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elif training_args.do_eval:
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column_names = datasets["validation"].column_names
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else:
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column_names = datasets["test"].column_names
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question_column_name = "question" if "question" in column_names else column_names[0]
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context_column_name = "context" if "context" in column_names else column_names[1]
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answer_column_name = "answers" if "answers" in column_names else column_names[2]
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# Padding side determines if we do (question|context) or (context|question).
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pad_on_right = tokenizer.padding_side == "right"
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if data_args.max_seq_length > tokenizer.model_max_length:
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logger.warning(
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f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
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f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
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)
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max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
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if data_args.pad_to_max_length or isinstance(training_args.strategy, tf.distribute.TPUStrategy):
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logger.info("Padding all batches to max length because argument was set or we're on TPU.")
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padding = "max_length"
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else:
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padding = False
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# Training preprocessing
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def prepare_train_features(examples):
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# Some of the questions have lots of whitespace on the left, which is not useful and will make the
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# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
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# left whitespace
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examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
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# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
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# in one example possible giving several features when a context is long, each of those features having a
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# context that overlaps a bit the context of the previous feature.
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tokenized_examples = tokenizer(
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examples[question_column_name if pad_on_right else context_column_name],
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examples[context_column_name if pad_on_right else question_column_name],
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truncation="only_second" if pad_on_right else "only_first",
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max_length=max_seq_length,
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stride=data_args.doc_stride,
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return_overflowing_tokens=True,
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return_offsets_mapping=True,
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padding=padding,
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)
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# Since one example might give us several features if it has a long context, we need a map from a feature to
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# its corresponding example. This key gives us just that.
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sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
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# The offset mappings will give us a map from token to character position in the original context. This will
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# help us compute the start_positions and end_positions.
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offset_mapping = tokenized_examples.pop("offset_mapping")
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# Let's label those examples!
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tokenized_examples["start_positions"] = []
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tokenized_examples["end_positions"] = []
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for i, offsets in enumerate(offset_mapping):
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# We will label impossible answers with the index of the CLS token.
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input_ids = tokenized_examples["input_ids"][i]
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cls_index = input_ids.index(tokenizer.cls_token_id)
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# Grab the sequence corresponding to that example (to know what is the context and what is the question).
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sequence_ids = tokenized_examples.sequence_ids(i)
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# One example can give several spans, this is the index of the example containing this span of text.
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sample_index = sample_mapping[i]
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answers = examples[answer_column_name][sample_index]
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# If no answers are given, set the cls_index as answer.
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if len(answers["answer_start"]) == 0:
|
|
tokenized_examples["start_positions"].append(cls_index)
|
|
tokenized_examples["end_positions"].append(cls_index)
|
|
else:
|
|
# Start/end character index of the answer in the text.
|
|
start_char = answers["answer_start"][0]
|
|
end_char = start_char + len(answers["text"][0])
|
|
|
|
# Start token index of the current span in the text.
|
|
token_start_index = 0
|
|
while sequence_ids[token_start_index] != (1 if pad_on_right else 0):
|
|
token_start_index += 1
|
|
|
|
# End token index of the current span in the text.
|
|
token_end_index = len(input_ids) - 1
|
|
while sequence_ids[token_end_index] != (1 if pad_on_right else 0):
|
|
token_end_index -= 1
|
|
|
|
# Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
|
|
if not (offsets[token_start_index][0] <= start_char and offsets[token_end_index][1] >= end_char):
|
|
tokenized_examples["start_positions"].append(cls_index)
|
|
tokenized_examples["end_positions"].append(cls_index)
|
|
else:
|
|
# Otherwise move the token_start_index and token_end_index to the two ends of the answer.
|
|
# Note: we could go after the last offset if the answer is the last word (edge case).
|
|
while token_start_index < len(offsets) and offsets[token_start_index][0] <= start_char:
|
|
token_start_index += 1
|
|
tokenized_examples["start_positions"].append(token_start_index - 1)
|
|
while offsets[token_end_index][1] >= end_char:
|
|
token_end_index -= 1
|
|
tokenized_examples["end_positions"].append(token_end_index + 1)
|
|
|
|
return tokenized_examples
|
|
|
|
processed_datasets = {}
|
|
if training_args.do_train:
|
|
if "train" not in datasets:
|
|
raise ValueError("--do_train requires a train dataset")
|
|
train_dataset = datasets["train"]
|
|
if data_args.max_train_samples is not None:
|
|
# We will select sample from whole data if argument is specified
|
|
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
|
|
train_dataset = train_dataset.select(range(max_train_samples))
|
|
# Create train feature from dataset
|
|
train_dataset = train_dataset.map(
|
|
prepare_train_features,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
)
|
|
if data_args.max_train_samples is not None:
|
|
# Number of samples might increase during Feature Creation, We select only specified max samples
|
|
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
|
|
train_dataset = train_dataset.select(range(max_train_samples))
|
|
processed_datasets["train"] = train_dataset
|
|
|
|
# Validation preprocessing
|
|
def prepare_validation_features(examples):
|
|
# Some of the questions have lots of whitespace on the left, which is not useful and will make the
|
|
# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
|
|
# left whitespace
|
|
examples[question_column_name] = [q.lstrip() for q in examples[question_column_name]]
|
|
|
|
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
|
|
# in one example possible giving several features when a context is long, each of those features having a
|
|
# context that overlaps a bit the context of the previous feature.
|
|
tokenized_examples = tokenizer(
|
|
examples[question_column_name if pad_on_right else context_column_name],
|
|
examples[context_column_name if pad_on_right else question_column_name],
|
|
truncation="only_second" if pad_on_right else "only_first",
|
|
max_length=max_seq_length,
|
|
stride=data_args.doc_stride,
|
|
return_overflowing_tokens=True,
|
|
return_offsets_mapping=True,
|
|
padding=padding,
|
|
)
|
|
|
|
# Since one example might give us several features if it has a long context, we need a map from a feature to
|
|
# its corresponding example. This key gives us just that.
|
|
sample_mapping = tokenized_examples.pop("overflow_to_sample_mapping")
|
|
|
|
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
|
|
# corresponding example_id and we will store the offset mappings.
|
|
tokenized_examples["example_id"] = []
|
|
|
|
for i in range(len(tokenized_examples["input_ids"])):
|
|
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
|
|
sequence_ids = tokenized_examples.sequence_ids(i)
|
|
context_index = 1 if pad_on_right else 0
|
|
|
|
# One example can give several spans, this is the index of the example containing this span of text.
|
|
sample_index = sample_mapping[i]
|
|
tokenized_examples["example_id"].append(examples["id"][sample_index])
|
|
|
|
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
|
|
# position is part of the context or not.
|
|
tokenized_examples["offset_mapping"][i] = [
|
|
(o if sequence_ids[k] == context_index else None)
|
|
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
|
|
]
|
|
|
|
return tokenized_examples
|
|
|
|
if training_args.do_eval:
|
|
if "validation" not in datasets:
|
|
raise ValueError("--do_eval requires a validation dataset")
|
|
eval_examples = datasets["validation"]
|
|
if data_args.max_eval_samples is not None:
|
|
# We will select sample from whole data
|
|
max_eval_samples = min(len(eval_examples), data_args.max_eval_samples)
|
|
eval_examples = eval_examples.select(range(max_eval_samples))
|
|
# Validation Feature Creation
|
|
eval_dataset = eval_examples.map(
|
|
prepare_validation_features,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
)
|
|
if data_args.max_eval_samples is not None:
|
|
# During Feature creation dataset samples might increase, we will select required samples again
|
|
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
|
|
eval_dataset = eval_dataset.select(range(max_eval_samples))
|
|
processed_datasets["validation"] = eval_dataset
|
|
|
|
if training_args.do_predict:
|
|
if "test" not in datasets:
|
|
raise ValueError("--do_predict requires a test dataset")
|
|
predict_examples = datasets["test"]
|
|
if data_args.max_predict_samples is not None:
|
|
# We will select sample from whole data
|
|
predict_examples = predict_examples.select(range(data_args.max_predict_samples))
|
|
# Predict Feature Creation
|
|
predict_dataset = predict_examples.map(
|
|
prepare_validation_features,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
)
|
|
if data_args.max_predict_samples is not None:
|
|
# During Feature creation dataset samples might increase, we will select required samples again
|
|
max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
|
|
predict_dataset = predict_dataset.select(range(max_predict_samples))
|
|
processed_datasets["test"] = predict_dataset
|
|
# endregion
|
|
|
|
# region Metrics and Post-processing:
|
|
def post_processing_function(examples, features, predictions, stage="eval"):
|
|
# Post-processing: we match the start logits and end logits to answers in the original context.
|
|
predictions = postprocess_qa_predictions(
|
|
examples=examples,
|
|
features=features,
|
|
predictions=predictions,
|
|
version_2_with_negative=data_args.version_2_with_negative,
|
|
n_best_size=data_args.n_best_size,
|
|
max_answer_length=data_args.max_answer_length,
|
|
null_score_diff_threshold=data_args.null_score_diff_threshold,
|
|
output_dir=training_args.output_dir,
|
|
prefix=stage,
|
|
)
|
|
# Format the result to the format the metric expects.
|
|
if data_args.version_2_with_negative:
|
|
formatted_predictions = [
|
|
{"id": k, "prediction_text": v, "no_answer_probability": 0.0} for k, v in predictions.items()
|
|
]
|
|
else:
|
|
formatted_predictions = [{"id": k, "prediction_text": v} for k, v in predictions.items()]
|
|
|
|
references = [{"id": ex["id"], "answers": ex[answer_column_name]} for ex in examples]
|
|
return EvalPrediction(predictions=formatted_predictions, label_ids=references)
|
|
|
|
metric = evaluate.load(
|
|
"squad_v2" if data_args.version_2_with_negative else "squad", cache_dir=model_args.cache_dir
|
|
)
|
|
|
|
def compute_metrics(p: EvalPrediction):
|
|
return metric.compute(predictions=p.predictions, references=p.label_ids)
|
|
|
|
# endregion
|
|
|
|
with training_args.strategy.scope():
|
|
dataset_options = tf.data.Options()
|
|
dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
|
|
num_replicas = training_args.strategy.num_replicas_in_sync
|
|
|
|
# region Load model and prepare datasets
|
|
if checkpoint is None:
|
|
model_path = model_args.model_name_or_path
|
|
else:
|
|
model_path = checkpoint
|
|
model = TFAutoModelForQuestionAnswering.from_pretrained(
|
|
model_path,
|
|
config=config,
|
|
cache_dir=model_args.cache_dir,
|
|
revision=model_args.model_revision,
|
|
token=model_args.token,
|
|
trust_remote_code=model_args.trust_remote_code,
|
|
)
|
|
if training_args.do_train:
|
|
training_dataset = model.prepare_tf_dataset(
|
|
processed_datasets["train"],
|
|
shuffle=True,
|
|
batch_size=training_args.per_device_train_batch_size * num_replicas,
|
|
tokenizer=tokenizer,
|
|
)
|
|
|
|
training_dataset = training_dataset.with_options(dataset_options)
|
|
|
|
num_train_steps = len(training_dataset) * training_args.num_train_epochs
|
|
if training_args.warmup_steps > 0:
|
|
num_warmup_steps = training_args.warmup_steps
|
|
elif training_args.warmup_ratio > 0:
|
|
num_warmup_steps = int(num_train_steps * training_args.warmup_ratio)
|
|
else:
|
|
num_warmup_steps = 0
|
|
|
|
optimizer, schedule = create_optimizer(
|
|
init_lr=training_args.learning_rate,
|
|
num_train_steps=len(training_dataset) * training_args.num_train_epochs,
|
|
num_warmup_steps=num_warmup_steps,
|
|
adam_beta1=training_args.adam_beta1,
|
|
adam_beta2=training_args.adam_beta2,
|
|
adam_epsilon=training_args.adam_epsilon,
|
|
weight_decay_rate=training_args.weight_decay,
|
|
adam_global_clipnorm=training_args.max_grad_norm,
|
|
)
|
|
|
|
# Transformers models compute the right loss for their task by default when labels are passed, and will
|
|
# use this for training unless you specify your own loss function in compile().
|
|
model.compile(optimizer=optimizer, jit_compile=training_args.xla, metrics=["accuracy"])
|
|
|
|
else:
|
|
# Optimizer doesn't matter as it won't be used anyway
|
|
model.compile(optimizer="sgd", jit_compile=training_args.xla, metrics=["accuracy"])
|
|
training_dataset = None
|
|
|
|
if training_args.do_eval:
|
|
eval_dataset = model.prepare_tf_dataset(
|
|
processed_datasets["validation"],
|
|
shuffle=False,
|
|
batch_size=training_args.per_device_train_batch_size * num_replicas,
|
|
tokenizer=tokenizer,
|
|
)
|
|
eval_dataset = eval_dataset.with_options(dataset_options)
|
|
else:
|
|
eval_dataset = None
|
|
|
|
if training_args.do_predict:
|
|
predict_dataset = model.prepare_tf_dataset(
|
|
processed_datasets["test"],
|
|
shuffle=False,
|
|
batch_size=training_args.per_device_eval_batch_size * num_replicas,
|
|
tokenizer=tokenizer,
|
|
)
|
|
predict_dataset = predict_dataset.with_options(dataset_options)
|
|
else:
|
|
predict_dataset = None
|
|
|
|
# endregion
|
|
|
|
# region Preparing push_to_hub and model card
|
|
push_to_hub_model_id = training_args.push_to_hub_model_id
|
|
model_name = model_args.model_name_or_path.split("/")[-1]
|
|
if not push_to_hub_model_id:
|
|
if data_args.dataset_name is not None:
|
|
push_to_hub_model_id = f"{model_name}-finetuned-{data_args.dataset_name}"
|
|
else:
|
|
push_to_hub_model_id = f"{model_name}-finetuned-question-answering"
|
|
|
|
model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "question-answering"}
|
|
if data_args.dataset_name is not None:
|
|
model_card_kwargs["dataset_tags"] = data_args.dataset_name
|
|
if data_args.dataset_config_name is not None:
|
|
model_card_kwargs["dataset_args"] = data_args.dataset_config_name
|
|
model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
|
|
else:
|
|
model_card_kwargs["dataset"] = data_args.dataset_name
|
|
|
|
if training_args.push_to_hub:
|
|
callbacks = [
|
|
PushToHubCallback(
|
|
output_dir=training_args.output_dir,
|
|
hub_model_id=push_to_hub_model_id,
|
|
hub_token=training_args.push_to_hub_token,
|
|
tokenizer=tokenizer,
|
|
**model_card_kwargs,
|
|
)
|
|
]
|
|
else:
|
|
callbacks = []
|
|
# endregion
|
|
|
|
# region Training and Evaluation
|
|
|
|
if training_args.do_train:
|
|
# Note that the validation and test datasets have been processed in a different way to the
|
|
# training datasets in this example, and so they don't have the same label structure.
|
|
# As such, we don't pass them directly to Keras, but instead get model predictions to evaluate
|
|
# after training.
|
|
model.fit(training_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks)
|
|
|
|
if training_args.do_eval:
|
|
logger.info("*** Evaluation ***")
|
|
|
|
# In this example, we compute advanced metrics at the end of training, but
|
|
# if you'd like to compute metrics every epoch that are too complex to be written as
|
|
# standard Keras metrics, you can use our KerasMetricCallback. See
|
|
# https://huggingface.co/docs/transformers/main/en/main_classes/keras_callbacks
|
|
|
|
eval_predictions = model.predict(eval_dataset)
|
|
if isinstance(eval_predictions.start_logits, tf.RaggedTensor):
|
|
# If predictions are RaggedTensor, we densify them. Since they are logits, padding with 0 is a bad idea!
|
|
# The reason is that a logit of 0 can often end up as quite a high probability value, sometimes even
|
|
# the highest probability in a sample. Instead, we use a large negative value, which ensures that the
|
|
# padding positions are correctly masked.
|
|
eval_start_logits = eval_predictions.start_logits.to_tensor(default_value=-1000).numpy()
|
|
eval_end_logits = eval_predictions.end_logits.to_tensor(default_value=-1000).numpy()
|
|
else:
|
|
eval_start_logits = eval_predictions.start_logits
|
|
eval_end_logits = eval_predictions.end_logits
|
|
|
|
post_processed_eval = post_processing_function(
|
|
datasets["validation"],
|
|
processed_datasets["validation"],
|
|
(eval_start_logits, eval_end_logits),
|
|
)
|
|
metrics = compute_metrics(post_processed_eval)
|
|
logging.info("Evaluation metrics:")
|
|
for metric, value in metrics.items():
|
|
logging.info(f"{metric}: {value:.3f}")
|
|
if training_args.output_dir is not None:
|
|
output_eval_file = os.path.join(training_args.output_dir, "all_results.json")
|
|
with open(output_eval_file, "w") as writer:
|
|
writer.write(json.dumps(metrics))
|
|
# endregion
|
|
|
|
# region Prediction
|
|
if training_args.do_predict:
|
|
logger.info("*** Predict ***")
|
|
|
|
test_predictions = model.predict(predict_dataset)
|
|
if isinstance(test_predictions.start_logits, tf.RaggedTensor):
|
|
# If predictions are RaggedTensor, we densify them. Since they are logits, padding with 0 is a bad idea!
|
|
# The reason is that a logit of 0 can often end up as quite a high probability value, sometimes even
|
|
# the highest probability in a sample. Instead, we use a large negative value, which ensures that the
|
|
# padding positions are correctly masked.
|
|
test_start_logits = test_predictions.start_logits.to_tensor(default_value=-1000).numpy()
|
|
test_end_logits = test_predictions.end_logits.to_tensor(default_value=-1000).numpy()
|
|
else:
|
|
test_start_logits = test_predictions.start_logits
|
|
test_end_logits = test_predictions.end_logits
|
|
post_processed_test = post_processing_function(
|
|
datasets["test"],
|
|
processed_datasets["test"],
|
|
(test_start_logits, test_end_logits),
|
|
)
|
|
metrics = compute_metrics(post_processed_test)
|
|
|
|
logging.info("Test metrics:")
|
|
for metric, value in metrics.items():
|
|
logging.info(f"{metric}: {value:.3f}")
|
|
# endregion
|
|
|
|
if training_args.output_dir is not None and not training_args.push_to_hub:
|
|
# If we're not pushing to hub, at least save a local copy when we're done
|
|
model.save_pretrained(training_args.output_dir)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|