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run_glue.py

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+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2020 The HuggingFace Inc. 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.
+"""Finetuning the library models for sequence classification on GLUE."""
+# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
+
+import os
+import re
+
+os.environ["WANDB_DISABLED"] = "true"
+
+# 设置CUDA设备
+os.environ['CUDA_VISIBLE_DEVICES'] = '2'
+
+
+import logging
+import os
+import random
+import sys
+from dataclasses import dataclass, field
+from typing import Optional
+
+import datasets
+import evaluate
+import numpy as np
+from datasets import load_dataset
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    EvalPrediction,
+    HfArgumentParser,
+    PretrainedConfig,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+    BertTokenizer,
+    BertModel
+)
+from transformers import BigBirdModel
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+from BertClsModel import BertForSequenceClassification
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+# check_min_version("4.43.0.dev0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
+
+task_to_keys = {
+    "cola": ("sentence", None),
+    "mnli": ("premise", "hypothesis"),
+    "mrpc": ("sentence1", "sentence2"),
+    "qnli": ("question", "sentence"),
+    "qqp": ("question1", "question2"),
+    "rte": ("sentence1", "sentence2"),
+    "sst2": ("sentence", None),
+    "stsb": ("sentence1", "sentence2"),
+    "wnli": ("sentence1", "sentence2"),
+}
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    task_name: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
+    )
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    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."
+            )
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
+    )
+    pad_to_max_length: bool = field(
+        default=True,
+        metadata={
+            "help": (
+                "Whether to pad all samples to `max_seq_length`. "
+                "If False, will pad the samples dynamically when batching to the maximum length in the batch."
+            )
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of training examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+                "value if set."
+            )
+        },
+    )
+    max_predict_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
+                "value if set."
+            )
+        },
+    )
+    train_file: Optional[str] = field(
+        default=None, metadata={"help": "A csv or a json file containing the training data."}
+    )
+    validation_file: Optional[str] = field(
+        default=None, metadata={"help": "A csv or a json file containing the validation data."}
+    )
+    test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})
+
+    def __post_init__(self):
+        if self.task_name is not None:
+            self.task_name = self.task_name.lower()
+            if self.task_name not in task_to_keys.keys():
+                raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
+        elif self.dataset_name is not None:
+            pass
+        elif self.train_file is None or self.validation_file is None:
+            raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.")
+        else:
+            train_extension = self.train_file.split(".")[-1]
+            assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+            validation_extension = self.validation_file.split(".")[-1]
+            assert (
+                validation_extension == train_extension
+            ), "`validation_file` should have the same extension (csv or json) as `train_file`."
+
+
+@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 huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether to trust the execution of code from datasets/models defined on the Hub."
+                " This option should only be set to `True` for repositories you trust and in which you have read the"
+                " code, as it will execute code present on the Hub on your local machine."
+            )
+        },
+    )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
+    )
+
+def data_padding(data_list, max_seq_length):
+    data_list_new = []
+    for dan_sentence in data_list:
+        padding_list = [0] * (max_seq_length - len(dan_sentence))
+        dan_sentence.extend(padding_list)
+        data_list_new.append(dan_sentence)
+        print("dan_sentence", len(dan_sentence))
+        print("=================================")
+    return data_list_new
+
+
+def tokenizer_ulit(tokenizer, data_list, padding, max_seq_length):
+    pattern = r"<\\Start>.*<\\End>"
+    # 7549
+    zhanweifu = "顯顯顯顯"
+    content_t_list = []
+    biaoji_list = []
+    for i in range(len(data_list)):
+        biaoji = re.findall(pattern, data_list[i], re.DOTALL)[0]
+        content_t = data_list[i].replace(biaoji, zhanweifu)
+        content_t_list.append(content_t)
+        biaoji_list.append(biaoji)
+
+    result_content_t_list = tokenizer(content_t_list, truncation=True)
+    result_biaoji_list = tokenizer(biaoji_list, truncation=True)
+
+    # print("result_content_t", result_content_t_list)
+    # print("result_biaoji", result_biaoji_list)
+
+    for index_content in range(len(result_content_t_list['input_ids'])):
+        result_biaoji_list['input_ids'][index_content] = result_biaoji_list['input_ids'][index_content][1:-1]
+        len_biaoji = len(result_biaoji_list['input_ids'][index_content])
+        result_biaoji_list['token_type_ids'][index_content] = [1] * len_biaoji
+        result_biaoji_list['attention_mask'][index_content] = [1] * len_biaoji
+
+        index_ = result_content_t_list['input_ids'][index_content].index(7549)
+
+        for _ in range(index_, index_ + 4):  # 循环删除下标3, 4, 5的元素
+            result_content_t_list['input_ids'][index_content].pop(index_)
+
+        for _ in range(index_, index_ + 4):  # 循环删除下标3, 4, 5的元素
+            result_content_t_list['token_type_ids'][index_content].pop(index_)
+
+        for _ in range(index_, index_ + 4):  # 循环删除下标3, 4, 5的元素
+            result_content_t_list['attention_mask'][index_content].pop(index_)
+
+        for i in range(len(result_biaoji_list['input_ids'][index_content])):
+            result_content_t_list['input_ids'][index_content].insert(index_ + i,
+                                                                     result_biaoji_list['input_ids'][index_content][i])
+
+        for i in range(len(result_biaoji_list['token_type_ids'][index_content])):
+            result_content_t_list['token_type_ids'][index_content].insert(index_ + i,
+                                                                          result_biaoji_list['token_type_ids'][
+                                                                              index_content][i])
+
+        for i in range(len(result_biaoji_list['attention_mask'][index_content])):
+            result_content_t_list['attention_mask'][index_content].insert(index_ + i,
+                                                                          result_biaoji_list['attention_mask'][
+                                                                              index_content][i])
+
+    result_content_t_list['input_ids'] = data_padding(result_content_t_list['input_ids'], max_seq_length=2048)
+    result_content_t_list['token_type_ids'] = data_padding(result_content_t_list['token_type_ids'], max_seq_length=2048)
+    result_content_t_list['attention_mask'] = data_padding(result_content_t_list['attention_mask'], max_seq_length=2048)
+    return result_content_t_list
+
+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()
+
+    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
+    # information sent is the one passed as arguments along with your Python/PyTorch versions.
+    send_example_telemetry("run_glue", model_args, data_args)
+    print("model_args", model_args)
+    9/0
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
+    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
+    #
+    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
+    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
+    # label if at least two columns are provided.
+    #
+    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
+    # single column. You can easily tweak this behavior (see below)
+    #
+    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
+    # download the dataset.
+    if data_args.task_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            "nyu-mll/glue",
+            data_args.task_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+        )
+    elif data_args.dataset_name is not None:
+        # Downloading and loading a dataset from the hub.
+        raw_datasets = load_dataset(
+            data_args.dataset_name,
+            data_args.dataset_config_name,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+            trust_remote_code=model_args.trust_remote_code,
+        )
+    else:
+        # Loading a dataset from your local files.
+        # CSV/JSON training and evaluation files are needed.
+        data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
+
+        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
+        # when you use `do_predict` without specifying a GLUE benchmark task.
+        if training_args.do_predict:
+            if data_args.test_file is not None:
+                train_extension = data_args.train_file.split(".")[-1]
+                test_extension = data_args.test_file.split(".")[-1]
+                assert (
+                    test_extension == train_extension
+                ), "`test_file` should have the same extension (csv or json) as `train_file`."
+                data_files["test"] = data_args.test_file
+            else:
+                raise ValueError("Need either a GLUE task or a test file for `do_predict`.")
+
+        for key in data_files.keys():
+            logger.info(f"load a local file for {key}: {data_files[key]}")
+
+        if data_args.train_file.endswith(".csv"):
+            # Loading a dataset from local csv files
+            raw_datasets = load_dataset(
+                "csv",
+                data_files=data_files,
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+            )
+        else:
+            # Loading a dataset from local json files
+            raw_datasets = load_dataset(
+                "json",
+                data_files=data_files,
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+            )
+    # See more about loading any type of standard or custom dataset at
+    # https://huggingface.co/docs/datasets/loading_datasets.
+
+    # Labels
+    if data_args.task_name is not None:
+        is_regression = data_args.task_name == "stsb"
+        if not is_regression:
+            label_list = raw_datasets["train"].features["label"].names
+            num_labels = len(label_list)
+        else:
+            num_labels = 1
+    else:
+        # Trying to have good defaults here, don't hesitate to tweak to your needs.
+        is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
+        if is_regression:
+            num_labels = 1
+        else:
+            # A useful fast method:
+            # https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique
+            label_list = raw_datasets["train"].unique("label")
+            label_list.sort()  # Let's sort it for determinism
+            num_labels = len(label_list)
+
+    # Load pretrained model and tokenizer
+    #
+    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    print(num_labels)
+    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,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    tokenizer = BertTokenizer.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_tokenizer,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    model = BertForSequenceClassification.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,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
+    )
+
+    # Preprocessing the raw_datasets
+    if data_args.task_name is not None:
+        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
+    else:
+        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
+        non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
+        if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
+            sentence1_key, sentence2_key = "sentence1", "sentence2"
+        else:
+            if len(non_label_column_names) >= 2:
+                sentence1_key, sentence2_key = non_label_column_names[:2]
+            else:
+                sentence1_key, sentence2_key = non_label_column_names[0], None
+
+    # Padding strategy
+    if data_args.pad_to_max_length:
+        padding = "max_length"
+    else:
+        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
+        padding = False
+
+    # Some models have set the order of the labels to use, so let's make sure we do use it.
+    label_to_id = None
+    if (
+        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
+        and data_args.task_name is not None
+        and not is_regression
+    ):
+        # Some have all caps in their config, some don't.
+        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
+        if sorted(label_name_to_id.keys()) == sorted(label_list):
+            label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)}
+        else:
+            logger.warning(
+                "Your model seems to have been trained with labels, but they don't match the dataset: ",
+                f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}."
+                "\nIgnoring the model labels as a result.",
+            )
+    elif data_args.task_name is None and not is_regression:
+        label_to_id = {v: i for i, v in enumerate(label_list)}
+
+    if label_to_id is not None:
+        model.config.label2id = label_to_id
+        model.config.id2label = {id: label for label, id in config.label2id.items()}
+    elif data_args.task_name is not None and not is_regression:
+        model.config.label2id = {l: i for i, l in enumerate(label_list)}
+        model.config.id2label = {id: label for label, id in config.label2id.items()}
+
+    if data_args.max_seq_length > tokenizer.model_max_length:
+        logger.warning(
+            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
+            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+        )
+    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+
+    def preprocess_function(examples):
+        print(examples)
+        print("1")
+        # print("examples[sentence1_key]", examples[sentence1_key])
+        # print("len(examples[sentence1_key])", len(examples[sentence1_key]))
+        # print("padding", padding)
+        # Tokenize the texts
+        args = (
+            (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
+        )
+        result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True)
+        # result = tokenizer_ulit(tokenizer, examples[sentence1_key], padding, max_seq_length)
+        # Map labels to IDs (not necessary for GLUE tasks)
+        if label_to_id is not None and "label" in examples:
+            result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
+        return result
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        raw_datasets = raw_datasets.map(
+            preprocess_function,
+            batched=True,
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on dataset",
+        )
+    if training_args.do_train:
+        if "train" not in raw_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = raw_datasets["train"]
+        if data_args.max_train_samples is not None:
+            max_train_samples = min(len(train_dataset), data_args.max_train_samples)
+            train_dataset = train_dataset.select(range(max_train_samples))
+
+    if training_args.do_eval:
+        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
+        if data_args.max_eval_samples is not None:
+            max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
+            eval_dataset = eval_dataset.select(range(max_eval_samples))
+
+    if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
+        if "test" not in raw_datasets and "test_matched" not in raw_datasets:
+            raise ValueError("--do_predict requires a test dataset")
+        predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"]
+        if data_args.max_predict_samples is not None:
+            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
+            predict_dataset = predict_dataset.select(range(max_predict_samples))
+
+    # Log a few random samples from the training set:
+    if training_args.do_train:
+        for index in random.sample(range(len(train_dataset)), 3):
+            logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+
+    # Get the metric function
+    if data_args.task_name is not None:
+        metric = evaluate.load("glue", data_args.task_name, cache_dir=model_args.cache_dir)
+    elif is_regression:
+        metric = evaluate.load("mse", cache_dir=model_args.cache_dir)
+    else:
+        metric = evaluate.load("./metrics/accuracy", cache_dir=model_args.cache_dir)
+
+    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
+    # predictions and label_ids field) and has to return a dictionary string to float.
+    def compute_metrics(p: EvalPrediction):
+        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
+        preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
+        result = metric.compute(predictions=preds, references=p.label_ids)
+        if len(result) > 1:
+            result["combined_score"] = np.mean(list(result.values())).item()
+        return result
+
+    # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
+    # we already did the padding.
+    if data_args.pad_to_max_length:
+        data_collator = default_data_collator
+    elif training_args.fp16:
+        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
+    else:
+        data_collator = None
+
+    # Initialize our Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        compute_metrics=compute_metrics,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+
+        # Loop to handle MNLI double evaluation (matched, mis-matched)
+        tasks = [data_args.task_name]
+        eval_datasets = [eval_dataset]
+        if data_args.task_name == "mnli":
+            tasks.append("mnli-mm")
+            valid_mm_dataset = raw_datasets["validation_mismatched"]
+            if data_args.max_eval_samples is not None:
+                max_eval_samples = min(len(valid_mm_dataset), data_args.max_eval_samples)
+                valid_mm_dataset = valid_mm_dataset.select(range(max_eval_samples))
+            eval_datasets.append(valid_mm_dataset)
+            combined = {}
+
+        for eval_dataset, task in zip(eval_datasets, tasks):
+            metrics = trainer.evaluate(eval_dataset=eval_dataset)
+
+            max_eval_samples = (
+                data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+            )
+            metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+
+            if task == "mnli-mm":
+                metrics = {k + "_mm": v for k, v in metrics.items()}
+            if task is not None and "mnli" in task:
+                combined.update(metrics)
+
+            trainer.log_metrics("eval", metrics)
+            trainer.save_metrics("eval", combined if task is not None and "mnli" in task else metrics)
+
+    if training_args.do_predict:
+        logger.info("*** Predict ***")
+
+        # Loop to handle MNLI double evaluation (matched, mis-matched)
+        tasks = [data_args.task_name]
+        predict_datasets = [predict_dataset]
+        if data_args.task_name == "mnli":
+            tasks.append("mnli-mm")
+            predict_datasets.append(raw_datasets["test_mismatched"])
+
+        for predict_dataset, task in zip(predict_datasets, tasks):
+            # Removing the `label` columns because it contains -1 and Trainer won't like that.
+            predict_dataset = predict_dataset.remove_columns("label")
+            predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
+            predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
+
+            output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt")
+            if trainer.is_world_process_zero():
+                with open(output_predict_file, "w") as writer:
+                    logger.info(f"***** Predict results {task} *****")
+                    writer.write("index\tprediction\n")
+                    for index, item in enumerate(predictions):
+                        if is_regression:
+                            writer.write(f"{index}\t{item:3.3f}\n")
+                        else:
+                            item = label_list[item]
+                            writer.write(f"{index}\t{item}\n")
+
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
+    if data_args.task_name is not None:
+        kwargs["language"] = "en"
+        kwargs["dataset_tags"] = "glue"
+        kwargs["dataset_args"] = data_args.task_name
+        kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()

run_train.sh

@@ -0,0 +1,11 @@
+python run_glue.py \
+  --model_name_or_path chinese_bert_wwm_ext_pytorch \
+  --train_file data/train_data_weipu.csv \
+  --validation_file data/dev_data_weipu.csv \
+  --do_train \
+  --do_eval \
+  --max_seq_length 512 \
+  --per_device_train_batch_size 32 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 5 \
+  --output_dir aigc_check

加载数据.py

@@ -0,0 +1,6 @@
+from datasets import load_dataset
+
+dataset = load_dataset('json', data_files='data/ceshi_3.json')
+print(dataset)  # 检查第一条数据
+print(dataset["train"][1])
+print(dataset["train"])

合并数据.py

@@ -0,0 +1,8 @@
+import pandas as pd
+
+data_1 = pd.read_csv("data/train_1_1_data_weipu.csv.csv").values.tolist()
+data_2 = pd.read_csv("data/train_2_2_data_weipu.csv.csv").values.tolist()
+
+data = data_1 + data_2
+
+pd.DataFrame(data, columns=["sentence", "label"]).to_csv("data/train_data_weipu_3.csv", index=False)

批量测试结果.py

@@ -0,0 +1,155 @@
+import json
+import os
+import re
+
+os.environ["WANDB_DISABLED"] = "true"
+
+
+# 设置CUDA设备
+os.environ['CUDA_VISIBLE_DEVICES'] = '1'
+
+
+import logging
+import os
+import random
+import sys
+from dataclasses import dataclass, field
+from typing import Optional
+
+import datasets
+import evaluate
+import numpy as np
+from datasets import load_dataset
+import torch
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    EvalPrediction,
+    HfArgumentParser,
+    PretrainedConfig,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+    BertTokenizer,
+    BertModel
+)
+from transformers import BigBirdModel
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+from BertClsModel import BertForSequenceClassification
+import pandas as pd
+from tqdm import tqdm
+
+
+def load_model(model_path: str):
+    config = AutoConfig.from_pretrained(
+        model_path,
+        num_labels=4,
+    )
+
+    tokenizer = BertTokenizer.from_pretrained(
+        model_path
+    )
+
+    model = BertForSequenceClassification.from_pretrained(
+        model_path,
+        config=config
+    )
+    return model, tokenizer
+
+id_2_lable = {
+    0: "正文",
+    1: "一级标题",
+    2: "二级标题",
+    3: "三级标题",
+}
+
+
+if __name__ == "__main__":
+    model, tokenizer = load_model(model_path='/home/majiahui/project/text-classification-long/long_paper_1')
+
+    # text = "(1)经病理学或细胞学确诊的肺癌患者；"
+    #
+    # sen = [text]
+    # result = tokenizer(sen, max_length=512, truncation=True)
+    # print(result)
+    #
+    # input_ids = result['input_ids']
+    # token_type_ids = result['token_type_ids']
+    #
+    # input_ids = seq_padding(tokenizer, input_ids)
+    # token_type_ids = seq_padding(tokenizer, token_type_ids)
+    #
+    #
+    # result = model(input_ids=input_ids,token_type_ids=token_type_ids)  # 这里不需要labels
+    # output = torch.sigmoid(result[0][0]).tolist()
+    # # result_ = result[0][0]
+    # print(output)
+
+    model.to("cuda")
+    data_list = pd.read_csv("data/long_paper_2.csv").values.tolist()
+    data_new = []
+
+    zong = 0
+    rel = 0
+    jishu = 0
+
+    for i in tqdm(data_list):
+        # print(zong)
+        # print(i)
+        zong += 1
+        text = i[0]
+        lable = i[1]
+
+        result = tokenizer([text], max_length=2048, truncation=True)
+
+        input_ids = result['input_ids']
+        token_type_ids = result['token_type_ids']
+        # print(input_ids)
+        # print(text)
+        # print(lable)
+        input_ids = torch.tensor(input_ids)  # 将列表转换为 PyTorch tensor
+        token_type_ids = torch.tensor(token_type_ids)  # 将列表转换为 PyTorch tensor
+        input_ids = input_ids.long()
+        token_type_ids = token_type_ids.long()
+
+        batch_masks = input_ids.gt(0).to("cuda")
+        input_ids, token_type_ids = input_ids.to("cuda"), token_type_ids.to("cuda")
+        result = model(input_ids=input_ids,token_type_ids=token_type_ids, attention_mask=batch_masks)  # 这里不需要labels
+        # output = torch.sigmoid(result[0][0]).tolist()
+        # # result_ = result[0][0]
+        # if output[1] > 0.5:
+        #     rel += 1
+        #
+        # data_new.append({
+        #     "index": index,
+        #     "text": text,
+        #     "acc": output,
+        # })
+
+        output = torch.sigmoid(result[0]).tolist()
+        # print(output)
+
+        # if output[0][0] > 0.60:
+        #     predict_lable = 0
+        # else:
+        #     predict_lable = 1
+
+        max_index = max(enumerate(output[0]), key=lambda x: x[1])[0]
+        # print(max_index)  # 输出最大值的下标
+        jishu +=1
+        data_new.append(json.dumps({
+            "text": text,
+            "label": id_2_lable[max_index],
+        }, ensure_ascii=False))
+    print(len(data_new))
+    with open("data/data_title_content.jsonl", "a", encoding="utf-8") as f:
+        for i in data_new:
+            f.write(i)
+            f.write("\n")

数据分割.py

@@ -0,0 +1,15 @@
+import pandas as pd
+import random
+
+
+data = pd.read_csv("data/train_data_weipu.csv").values.tolist()
+random.shuffle(data)
+random.shuffle(data)
+
+split = int(len(data)*0.5)
+train_1 = data[:split]
+train_2 = data[split:]
+
+pd.DataFrame(train_1, columns=["sentence", "label"]).to_csv("data/train_1_data_weipu.csv", index=False)
+pd.DataFrame(train_2, columns=["sentence", "label"]).to_csv("data/train_2_data_weipu.csv", index=False)
+

数据处理.py

@@ -0,0 +1,39 @@
+import json
+'''
+{"text": "EU rejects German call to boycott British lamb.", "label": [[0, 2, "ORG"]]}
+{"text": "Peter Blackburn", "label": [[0, 15, "PERSON"]]}
+{"text": "President Obama", "label": [[10, 15, "PERSON"]]}
+'''
+with open("data/zc_4.jsonl", encoding="utf-8") as f:
+    data = f.readlines()
+
+data_new = []
+
+for i in data:
+    data_dan_json = json.loads(i)
+    if len(data_dan_json["label"]) >1 or len(data_dan_json["label"]) == 0:
+        continue
+    text = data_dan_json["text"]
+    print(text)
+    print(data_dan_json)
+
+    start = str(text).index("<\\Start>")
+    end = str(text).index("<\\End>")
+
+    print(start)
+    print(end)
+    text = str(text).replace("<\\Start>", "").replace("<\\End>", "")
+
+    label = []
+    if data_dan_json['label'][0] != '正文':
+        label.append([start, end-8, data_dan_json['label'][0]])
+
+    data_new.append({
+        "text": text,
+        "label": label
+    })
+
+with open("data/zc_ner.jsonl", "a", encoding="utf-8") as f:
+    for i in data_new:
+        f.write(json.dumps(i, ensure_ascii=False))
+        f.write("\n")

测试paperred降aigc检测结果.py

@@ -0,0 +1,135 @@
+import os
+os.environ["WANDB_DISABLED"] = "true"
+os.environ["CUDA_VISIBLE_DEVICES"] = "2"
+import logging
+import random
+import sys
+import warnings
+from dataclasses import dataclass, field
+from typing import Optional
+import json
+import datasets
+import evaluate
+import numpy as np
+from datasets import load_dataset
+import torch
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    BertForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    EvalPrediction,
+    HfArgumentParser,
+    PretrainedConfig,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+
+
+def load_model(config_path: str, model_path: str):
+    model = AutoModelForSequenceClassification.from_pretrained(model_path)
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    return model, tokenizer
+
+
+def seq_padding(tokenizer, X):
+    pad_id = tokenizer.convert_tokens_to_ids("[PAD]")
+    if len(X) <= 1:
+        return torch.tensor(X)
+    L = [len(x) for x in X]
+    ML = max(L)
+    X = torch.Tensor([x + [pad_id] * (ML - len(x)) if len(x) < ML else x for x in X])
+    return X
+
+
+if __name__ == "__main__":
+    model, tokenizer = load_model(config_path='chinese_bert_wwm_ext_pytorch/config.json',
+                       model_path='aigc_check')
+
+    # text = "(1)经病理学或细胞学确诊的肺癌患者；"
+    #
+    # sen = [text]
+    # result = tokenizer(sen, max_length=512, truncation=True)
+    # print(result)
+    #
+    # input_ids = result['input_ids']
+    # token_type_ids = result['token_type_ids']
+    #
+    # input_ids = seq_padding(tokenizer, input_ids)
+    # token_type_ids = seq_padding(tokenizer, token_type_ids)
+    #
+    #
+    # result = model(input_ids=input_ids,token_type_ids=token_type_ids)  # 这里不需要labels
+    # output = torch.sigmoid(result[0][0]).tolist()
+    # # result_ = result[0][0]
+    # print(output)
+
+    model.to("cuda")
+    with open("data/paperred_aigc_cls.json", encoding='utf8') as f:
+        data = json.loads(f.read())
+
+
+    data_new = []
+
+    zong = 0
+    rel = 0
+    jishu = 0
+
+    for index, i in enumerate(data[:10000]):
+        print(zong)
+        zong += 1
+        text1 = i["input"]
+        text2 = i["output"]
+
+        sen = [text1, text2]
+        result = tokenizer(sen, max_length=512, truncation=True)
+
+        input_ids = result['input_ids']
+        token_type_ids = result['token_type_ids']
+
+        input_ids = seq_padding(tokenizer, input_ids)
+        token_type_ids = seq_padding(tokenizer, token_type_ids)
+
+        input_ids = input_ids.long()
+        token_type_ids = token_type_ids.long()
+
+        batch_masks = input_ids.gt(0).to("cuda")
+        input_ids, token_type_ids = input_ids.to("cuda"), token_type_ids.to("cuda")
+        result = model(input_ids=input_ids,token_type_ids=token_type_ids, attention_mask=batch_masks)  # 这里不需要labels
+        # output = torch.sigmoid(result[0][0]).tolist()
+        # # result_ = result[0][0]
+        # if output[1] > 0.5:
+        #     rel += 1
+        #
+        # data_new.append({
+        #     "index": index,
+        #     "text": text,
+        #     "acc": output,
+        # })
+
+        output = torch.sigmoid(result[0]).tolist()
+        if output[0][1] > 0.5 and output[1][1] < 0.5:
+            jishu +=1
+            data_new.append({
+                "index": index,
+                "text1": text1,
+                "text2": text2,
+                "acc": [output[0][1], output[1][1]],
+            })
+
+    print(jishu)
+    data_dict = {
+        # "acc" : rel/zong,
+        "data": data_new
+    }
+
+    with open("data/paperred_aigc_acc3.json", "w", encoding='utf8') as f:
+        json.dump(data_dict, f, ensure_ascii=False, indent=4)

测试分割数据.py

@@ -0,0 +1,108 @@
+import tensorflow as tf
+import numpy as np
+import os
+
+conll2003_path = "D:/ml/conll2003"
+
+
+def load_file(path="/train.txt"):
+    # Load the dataset
+    train_sentences = []
+    train_labels = []
+    with open(conll2003_path + path) as f:
+        sentence = []
+        labels = []
+        for line in f:
+            line = line.strip()
+            if line:
+                word, pos, chunk, label = line.split()
+                sentence.append(word)
+                labels.append(label)
+            else:
+                train_sentences.append(sentence)
+                train_labels.append(labels)
+                sentence = []
+                labels = []
+
+    return train_sentences, train_labels
+
+
+    return X, y
+
+
+def get_dataset():
+    # Load the dataset
+    train_sentences, train_labels = load_file("/train.txt")
+    valid_sentences, valid_labels = load_file("/valid.txt")
+    test_sentences, test_labels = load_file("/test.txt")
+    # Create vocabulary and tag dictionaries
+    all_sentencses = np.concatenate([train_sentences, valid_sentences, test_sentences])
+    all_labels = np.concatenate([train_labels, valid_labels, test_labels])
+    vocab = set()
+    tags = set()
+    for sentence in all_sentencses:
+        for word in sentence:
+            vocab.add(word.lower())
+    for labels in all_labels:
+        for label in labels:
+            tags.add(label)
+
+    word2idx = {w: i + 1 for i, w in enumerate(vocab)}
+    tag2idx = {t: i for i, t in enumerate(tags)}
+
+    save_dict(word2idx, os.path.join('datasetpath', 'word2idx.json'))
+    save_dict(tag2idx, os.path.join('datasetpath', 'idx2Label.json'))
+
+    num_words = len(word2idx) + 1
+    num_tags = len(tag2idx)
+
+    train_X, train_y = preproces(word2idx, tag2idx, num_tags, train_sentences, train_labels);
+    valid_X, valid_y = preproces(word2idx, tag2idx, num_tags, valid_sentences, valid_labels);
+    test_X, test_y = preproces(word2idx, tag2idx, num_tags, test_sentences, test_labels);
+
+    np.savez(os.path.join('datasetpath', 'dataset.npz'), train_X=train_X, train_y=train_y, valid_X=valid_X,
+             valid_y=valid_y, test_X=test_X, test_y=test_y)
+    return train_X, train_y, valid_X, valid_y, test_X, test_y
+
+
+def load_dataset():
+    dataset = np.load(os.path.join('datasetpath', 'dataset.npz'))
+    train_X = dataset['train_X']
+    train_y = dataset['train_y']
+    valid_X = dataset['valid_X']
+    valid_y = dataset['valid_y']
+    test_X = dataset['test_X']
+    test_y = dataset['test_y']
+    return train_X, train_y, valid_X, valid_y, test_X, test_y
+
+
+max_len = 64
+
+
+def save_dict(dict, file_path):
+    import json
+    # Saving the dictionary to a file
+    with open(file_path, 'w') as f:
+        json.dump(dict, f)
+
+
+def load_dict(path_file):
+    import json
+
+    # Loading the dictionary from the file
+    with open(path_file, 'r') as f:
+        loaded_dict = json.load(f)
+        return loaded_dict;
+
+    print(loaded_dict)  # Output: {'key1': 'value1', 'key2': 'value2'}
+
+
+if __name__ == '__main__':
+    get_dataset()
+    train_X, train_y, valid_X, valid_y, test_X, test_y = load_dataset()
+
+    print(len(train_X))
+    print(len(train_y))
+
+    print(np.array(train_X).shape)
+    print(np.array(train_y).shape)

测试分词.py

@@ -0,0 +1,22 @@
+from transformers import (
+    AutoConfig,
+    AutoModelForTokenClassification,
+    AutoTokenizer,
+    DataCollatorForTokenClassification,
+    HfArgumentParser,
+    PretrainedConfig,
+    PreTrainedTokenizerFast,
+    Trainer,
+    TrainingArguments,
+    set_seed,
+    BertTokenizer,
+    BertTokenizerFast,
+    BertModel
+)
+
+tokenizer = BertTokenizerFast.from_pretrained(
+    "/home/majiahui/project/models-llm/chinese-bigbird-wwm-base-4096",
+    use_fast=True,
+    revision="main",
+    trust_remote_code=False,
+)

生成ABtest训练数据.py

@@ -0,0 +1,132 @@
+import os
+os.environ["WANDB_DISABLED"] = "true"
+os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+import logging
+import random
+import sys
+import warnings
+from dataclasses import dataclass, field
+from typing import Optional
+import json
+import datasets
+import evaluate
+import numpy as np
+from datasets import load_dataset
+import torch
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    BertForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    EvalPrediction,
+    HfArgumentParser,
+    PretrainedConfig,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+import pandas as pd
+from tqdm import tqdm
+
+
+def load_model(config_path: str, model_path: str):
+    model = AutoModelForSequenceClassification.from_pretrained(model_path)
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    return model, tokenizer
+
+
+def seq_padding(tokenizer, X):
+    pad_id = tokenizer.convert_tokens_to_ids("[PAD]")
+    if len(X) <= 1:
+        return torch.tensor(X)
+    L = [len(x) for x in X]
+    ML = max(L)
+    X = torch.Tensor([x + [pad_id] * (ML - len(x)) if len(x) < ML else x for x in X])
+    return X
+
+
+if __name__ == "__main__":
+    model, tokenizer = load_model(config_path='chinese_bert_wwm_ext_pytorch/config.json',
+                       model_path='aigc_check_6')
+
+    # text = "(1)经病理学或细胞学确诊的肺癌患者；"
+    #
+    # sen = [text]
+    # result = tokenizer(sen, max_length=512, truncation=True)
+    # print(result)
+    #
+    # input_ids = result['input_ids']
+    # token_type_ids = result['token_type_ids']
+    #
+    # input_ids = seq_padding(tokenizer, input_ids)
+    # token_type_ids = seq_padding(tokenizer, token_type_ids)
+    #
+    #
+    # result = model(input_ids=input_ids,token_type_ids=token_type_ids)  # 这里不需要labels
+    # output = torch.sigmoid(result[0][0]).tolist()
+    # # result_ = result[0][0]
+    # print(output)
+
+    model.to("cuda")
+    data_list = pd.read_csv("data/train_1_data_weipu.csv").values.tolist()
+
+    data_new = []
+
+    zong = 0
+    rel = 0
+    jishu = 0
+
+    for i in tqdm(data_list):
+        # print(zong)
+        zong += 1
+        text = i[0]
+        lable = i[1]
+
+        result = tokenizer([text], max_length=512, truncation=True)
+
+        input_ids = result['input_ids']
+        token_type_ids = result['token_type_ids']
+        # print(input_ids)
+        # print(text)
+        # print(lable)
+        input_ids = seq_padding(tokenizer, input_ids)
+        token_type_ids = seq_padding(tokenizer, token_type_ids)
+
+        input_ids = input_ids.long()
+        token_type_ids = token_type_ids.long()
+
+        batch_masks = input_ids.gt(0).to("cuda")
+        input_ids, token_type_ids = input_ids.to("cuda"), token_type_ids.to("cuda")
+        result = model(input_ids=input_ids,token_type_ids=token_type_ids, attention_mask=batch_masks)  # 这里不需要labels
+        # output = torch.sigmoid(result[0][0]).tolist()
+        # # result_ = result[0][0]
+        # if output[1] > 0.5:
+        #     rel += 1
+        #
+        # data_new.append({
+        #     "index": index,
+        #     "text": text,
+        #     "acc": output,
+        # })
+
+        output = torch.sigmoid(result[0]).tolist()
+        # print(output)
+
+        if output[0][0] > 0.50:
+            predict_lable = 0
+        else:
+            predict_lable = 1
+        jishu +=1
+
+        if predict_lable == lable:
+            data_new.append([
+                text,
+                lable
+            ])
+    pd.DataFrame(data_new, columns=["sentence", "label"]).to_csv("data/train_2_2_data_weipu.csv.csv", index=False)

生成文本.py

@@ -0,0 +1,14 @@
+import pandas as pd
+
+
+data = pd.read_csv("data/dev_data_weipu.csv", encoding="utf-8").values.tolist()
+
+data_new = ""
+for i in data[:100]:
+    data_new += i[0]
+    data_new += "\n"
+
+print(data_new)
+
+with open("data/ceshi_aigc_weipu.txt", "w", encoding="utf-8") as f:
+    f.write(data_new)

计算肉斤数.py

@@ -0,0 +1,2 @@
+a = 16+ 16 + 16 + 7+ 7 + 7+ 11
+print(a)

读取mysql文件.py

@@ -0,0 +1,33 @@
+# 定义目标表名
+import time
+
+target_tables = [
+    "spider_latest_journal_paper_list",
+    "spider_latest_qikan_column_list",
+    "spider_latest_qikan_list"
+]
+
+# 输出文件保存路径
+output_file = "found_tables.sql"
+
+# 初始化变量
+found_lines = []
+
+
+# 逐行读取文件
+with open("/home/majiahui/project/mysql_file_zw_paper/fabiao.sql", "r", encoding="utf-8") as file:
+    for line_number, line in enumerate(file, start=1):
+        if line_number%10000 == 0:
+            print("==============================================")
+            print(line_number)
+            print(time.time())
+        for table in target_tables:
+            if table in line:
+                found_lines.append(f"Line {line_number}: {line.strip()}")
+                break
+
+# 保存匹配结果到文件
+with open(output_file, "w", encoding="utf-8") as outfile:
+    outfile.write("\n".join(found_lines))
+
+print(f"搜索完成，共找到 {len(found_lines)} 行，结果已保存到 {output_file}")

读取文件.py

@@ -0,0 +1,6 @@
+with open('found_tables.sql', 'r') as file:
+    for i, line in enumerate(file):
+        if i < 7:
+            print(line.strip())
+        else:
+            break