PDF或图片文档内容识别、关系抽取-Toy模板网

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需求

自动识别法院和公积金中心的文书（调解书、判决书、裁定书、通知书）扫描件（PDF或图片），获取特定结构的数据，自动对比。抽取结构如：

['标题','诉讼案号','执行案号','公积金',{'原告': ['姓名', '单位', '生日', '身份证号']},{'被告': ['姓名', '单位', '生日', '身份证号']}]

思路

# 1.遍历目录获取源文件名；
# 2.统一格式，PDF转JPG。自动识别图片方向，把图片转正；
# 3.排序并获取整篇结构化内容，写TXT文件输出；
# 4.导入 Label Studio 标记；
# 5.导出 JSON 格式标记样本；
# 6.将 label studio 导出的 JSON 数据文件格式转换成 doccano 导出的数据文件格式；
# 7.构造网络训练；
# 8.部署模型预测。

环境

Ubuntu 22.04
Anaconda
PaddlePaddle
Label Studio

步骤

预处理依赖

import os
from pdf2image import convert_from_path
from paddleclas import PaddleClas
from PIL import Image
import numpy as np
from paddleocr import PaddleOCR

加载OCR模型

clas_engine = PaddleClas(model_name="text_image_orientation", use_gpu=False)
# 加载OCR模型
ocr = PaddleOCR(ocr_version='PP-OCRv3')

遍历文件

def get_file_names(directory):
    file_names = []
    for root, dirs, files in os.walk(directory):
        for file in files:
            file_names.append(os.path.join(root, file))
    return file_names

获取文件后缀名

def get_file_extension(file_name):
    _, extension = os.path.splitext(file_name)
    return extension

pdf转jpg

def pdf_to_jpg(pdf_path, output_folder):
    images = convert_from_path(pdf_path)
    file_names = []
    for i, image in enumerate(images):
        str = f"{output_folder}/page_{i+1}.jpg"
        image.save(str, "JPEG")
        file_names.append(str)
    return file_names

自动创建目录

def create_directory(directory):
    if not os.path.exists(directory):
        os.makedirs(directory)
        print(f"目录 '{directory}' 创建成功")
    else:
        print(f"目录 '{directory}' 已存在")

识别图片方向

def model_inference_direction(image) -> tuple:
    results = clas_engine.predict(image, print_pred=True)
    try:
        # 可能有无结果的，直接取值会报错，try忽略掉
        results = list(results)[0][0]
        results = int(results["label_names"][0])
    except Exception as e:
        print("An error occurred:", str(e))
    return results

识别文本

def ocr_gettext(image):
    results = ocr.ocr(image)
    text = ""
    for result in results:
        for res in result:
            # print(res[1][0])
            text = text + res[1][0]
    return text

执行

# 指定目录路径
directory_path = "D:/jerry/code/python/rap/dataset/test/"

# 获取目录及其子目录下的文件名
file_names = get_file_names(directory_path)

for file_name in file_names:
    suffix_name = get_file_extension(file_name)
    # 忽略大小写对比
    if suffix_name.lower() == '.pdf'.lower():
        # 调用函数进行转换
        jpg_output = file_name.replace(".pdf","")
        create_directory(jpg_output)
        fnames = pdf_to_jpg(file_name, jpg_output)
        file_names.extend(fnames)
    elif suffix_name.lower() == '.txt'.lower():
        os.remove(file_name)
        file_names.remove(file_name)
    else:
        image = Image.open(file_name)
        # 可能有4分量的，强制转为3分量
        image = image.convert("RGB")
        # 自动识别图片旋转角度（顺90 识别不准）
        angle = model_inference_direction(np.asarray(image))
        # 识别出参： 正角度为顺时针旋转
        # 执行入参：正角度为逆时针旋转
        image = image.rotate(angle)
        # image.show()
        text = ocr_gettext(np.asarray(image))
        text_out = file_name.replace(suffix_name, ".txt")
        with open(text_out, "w", encoding="utf-8") as file:
            file.write(text)

搭建label studio标记，标记完成后导出JSON。

PDF或图片文档内容识别、关系抽取

Label Studio JSON转Doccano JSON

# Copyright (c) 2022 PaddlePaddle Authors. 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.

import argparse
import os
import json


def append_attrs(data, item, label_id, relation_id):

    mapp = {}

    for anno in data["annotations"][0]["result"]:
        if anno["type"] == "labels":
            label_id += 1
            item["entities"].append(
                {
                    "id": label_id,
                    "label": anno["value"]["labels"][0],
                    "start_offset": anno["value"]["start"],
                    "end_offset": anno["value"]["end"],
                }
            )
            mapp[anno["id"]] = label_id

    for anno in data["annotations"][0]["result"]:
        if anno["type"] == "relation":
            relation_id += 1
            item["relations"].append(
                {
                    "id": relation_id,
                    "from_id": mapp[anno["from_id"]],
                    "to_id": mapp[anno["to_id"]],
                    "type": anno["labels"][0],
                }
            )

    return item, label_id, relation_id


def convert(dataset):
    results = []
    outer_id = 0
    label_id = 0
    relation_id = 0
    for data in dataset:
        labels = data["annotations"][0]["result"]
        outer_id += 1
        item = {"id": outer_id, "text": data["data"]["value"], "entities": [], "relations": []}
        item, label_id, relation_id = append_attrs(data, item, label_id, relation_id)
        results.append(item)

    return results


def do_convert(labelstudio_file, doccano_file):
    with open(labelstudio_file, "r", encoding="utf-8") as infile:
        for content in infile:
            dataset = json.loads(content)
        results = convert(dataset)
        print(results)

    with open(doccano_file, "w", encoding="utf-8") as outfile:
        for item in results:
            outline = json.dumps(item, ensure_ascii=False)
            outfile.write(outline + "\n")



labelstudio_file = "data/project-11-at-2023-06-07-08-12-d7affacb.json"
doccano_file = "data/doccano_ext.json"
do_convert(labelstudio_file, doccano_file)

python3 ./labelstudio2doccano.py

构造数据集

# coding=utf-8
# Copyright (c) 2022 PaddlePaddle Authors. 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.

import argparse
import json
import os
import time
from decimal import Decimal

import numpy as np
from utils import convert_cls_examples, convert_ext_examples, set_seed

from paddlenlp.trainer.argparser import strtobool
from paddlenlp.utils.log import logger


def do_convert():
    set_seed(args.seed)

    tic_time = time.time()
    if not os.path.exists(args.doccano_file):
        raise ValueError("Please input the correct path of doccano file.")

    if not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)

    if len(args.splits) != 0 and len(args.splits) != 3:
        raise ValueError("Only []/ len(splits)==3 accepted for splits.")

    def _check_sum(splits):
        return Decimal(str(splits[0])) + Decimal(str(splits[1])) + Decimal(str(splits[2])) == Decimal("1")

    if len(args.splits) == 3 and not _check_sum(args.splits):
        raise ValueError("Please set correct splits, sum of elements in splits should be equal to 1.")

    with open(args.doccano_file, "r", encoding="utf-8") as f:
        raw_examples = f.readlines()

    def _create_ext_examples(
        examples,
        negative_ratio,
        prompt_prefix="情感倾向",
        options=["正向", "负向"],
        separator="##",
        shuffle=False,
        is_train=True,
        schema_lang="ch",
    ):
        entities, relations, aspects = convert_ext_examples(
            examples, negative_ratio, prompt_prefix, options, separator, is_train, schema_lang
        )
        examples = entities + relations + aspects
        if shuffle:
            indexes = np.random.permutation(len(examples))
            examples = [examples[i] for i in indexes]
        return examples

    def _create_cls_examples(examples, prompt_prefix, options, shuffle=False):
        examples = convert_cls_examples(examples, prompt_prefix, options)
        if shuffle:
            indexes = np.random.permutation(len(examples))
            examples = [examples[i] for i in indexes]
        return examples

    def _save_examples(save_dir, file_name, examples):
        count = 0
        save_path = os.path.join(save_dir, file_name)
        with open(save_path, "w", encoding="utf-8") as f:
            for example in examples:
                f.write(json.dumps(example, ensure_ascii=False) + "\n")
                count += 1
        logger.info("Save %d examples to %s." % (count, save_path))

    if len(args.splits) == 0:
        if args.task_type == "ext":
            examples = _create_ext_examples(
                raw_examples,
                args.negative_ratio,
                args.prompt_prefix,
                args.options,
                args.separator,
                args.is_shuffle,
                schema_lang=args.schema_lang,
            )
        else:
            examples = _create_cls_examples(raw_examples, args.prompt_prefix, args.options, args.is_shuffle)
        _save_examples(args.save_dir, "train.txt", examples)
    else:
        if args.is_shuffle:
            indexes = np.random.permutation(len(raw_examples))
            index_list = indexes.tolist()
            raw_examples = [raw_examples[i] for i in indexes]
        else:
            index_list = list(range(len(raw_examples)))

        i1, i2, _ = args.splits
        p1 = int(len(raw_examples) * i1)
        p2 = int(len(raw_examples) * (i1 + i2))

        train_ids = index_list[:p1]
        dev_ids = index_list[p1:p2]
        test_ids = index_list[p2:]

        with open(os.path.join(args.save_dir, "sample_index.json"), "w") as fp:
            maps = {"train_ids": train_ids, "dev_ids": dev_ids, "test_ids": test_ids}
            fp.write(json.dumps(maps))

        if args.task_type == "ext":
            train_examples = _create_ext_examples(
                raw_examples[:p1],
                args.negative_ratio,
                args.prompt_prefix,
                args.options,
                args.separator,
                args.is_shuffle,
                schema_lang=args.schema_lang,
            )
            dev_examples = _create_ext_examples(
                raw_examples[p1:p2],
                -1,
                args.prompt_prefix,
                args.options,
                args.separator,
                is_train=False,
                schema_lang=args.schema_lang,
            )
            test_examples = _create_ext_examples(
                raw_examples[p2:],
                -1,
                args.prompt_prefix,
                args.options,
                args.separator,
                is_train=False,
                schema_lang=args.schema_lang,
            )
        else:
            train_examples = _create_cls_examples(raw_examples[:p1], args.prompt_prefix, args.options)
            dev_examples = _create_cls_examples(raw_examples[p1:p2], args.prompt_prefix, args.options)
            test_examples = _create_cls_examples(raw_examples[p2:], args.prompt_prefix, args.options)

        _save_examples(args.save_dir, "train.txt", train_examples)
        _save_examples(args.save_dir, "dev.txt", dev_examples)
        _save_examples(args.save_dir, "test.txt", test_examples)

    logger.info("Finished! It takes %.2f seconds" % (time.time() - tic_time))


if __name__ == "__main__":
    # yapf: disable
    parser = argparse.ArgumentParser()

    parser.add_argument("--doccano_file", default="./data/doccano_ext.json", type=str, help="The doccano file exported from doccano platform.")
    parser.add_argument("--save_dir", default="./data", type=str, help="The path of data that you wanna save.")
    parser.add_argument("--negative_ratio", default=5, type=int, help="Used only for the extraction task, the ratio of positive and negative samples, number of negtive samples = negative_ratio * number of positive samples")
    parser.add_argument("--splits", default=[0.8, 0.1, 0.1], type=float, nargs="*", help="The ratio of samples in datasets. [0.6, 0.2, 0.2] means 60% samples used for training, 20% for evaluation and 20% for test.")
    parser.add_argument("--task_type", choices=['ext', 'cls'], default="ext", type=str, help="Select task type, ext for the extraction task and cls for the classification task, defaults to ext.")
    parser.add_argument("--options", default=["正向", "负向"], type=str, nargs="+", help="Used only for the classification task, the options for classification")
    parser.add_argument("--prompt_prefix", default="情感倾向", type=str, help="Used only for the classification task, the prompt prefix for classification")
    parser.add_argument("--is_shuffle", default="True", type=strtobool, help="Whether to shuffle the labeled dataset, defaults to True.")
    parser.add_argument("--seed", type=int, default=1000, help="Random seed for initialization")
    parser.add_argument("--separator", type=str, default='##', help="Used only for entity/aspect-level classification task, separator for entity label and classification label")
    parser.add_argument("--schema_lang", choices=["ch", "en"], default="ch", help="Select the language type for schema.")

    args = parser.parse_args()
    # yapf: enable

    do_convert()

python3 doccano.py --doccano_file ./data/doccano_ext.json --task_type ext --save_dir ./data --splits 0.8 0.2 0 --schema_lang ch

PDF或图片文档内容识别、关系抽取

工具，命名为utils.py

# Copyright (c) 2022 PaddlePaddle Authors. 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.

import json
import math
import random
import re
from typing import List, Optional

import numpy as np
import paddle
from tqdm import tqdm

from paddlenlp.utils.log import logger


def set_seed(seed):
    paddle.seed(seed)
    random.seed(seed)
    np.random.seed(seed)


def create_data_loader(dataset, mode="train", batch_size=1, trans_fn=None):
    """
    Create dataloader.
    Args:
        dataset(obj:`paddle.io.Dataset`): Dataset instance.
        mode(obj:`str`, optional, defaults to obj:`train`): If mode is 'train', it will shuffle the dataset randomly.
        batch_size(obj:`int`, optional, defaults to 1): The sample number of a mini-batch.
        trans_fn(obj:`callable`, optional, defaults to `None`): function to convert a data sample to input ids, etc.
    Returns:
        dataloader(obj:`paddle.io.DataLoader`): The dataloader which generates batches.
    """
    if trans_fn:
        dataset = dataset.map(trans_fn)

    shuffle = True if mode == "train" else False
    if mode == "train":
        sampler = paddle.io.DistributedBatchSampler(dataset=dataset, batch_size=batch_size, shuffle=shuffle)
    else:
        sampler = paddle.io.BatchSampler(dataset=dataset, batch_size=batch_size, shuffle=shuffle)
    dataloader = paddle.io.DataLoader(dataset, batch_sampler=sampler, return_list=True)
    return dataloader


def map_offset(ori_offset, offset_mapping):
    """
    map ori offset to token offset
    """
    for index, span in enumerate(offset_mapping):
        if span[0] <= ori_offset < span[1]:
            return index
    return -1


def reader(data_path, max_seq_len=512):
    """
    read json
    """
    with open(data_path, "r", encoding="utf-8") as f:
        for line in f:
            json_line = json.loads(line)
            content = json_line["content"].strip()
            prompt = json_line["prompt"]
            # Model Input is aslike: [CLS] Prompt [SEP] Content [SEP]
            # It include three summary tokens.
            if max_seq_len <= len(prompt) + 3:
                raise ValueError("The value of max_seq_len is too small, please set a larger value")
            max_content_len = max_seq_len - len(prompt) - 3
            if len(content) <= max_content_len:
                yield json_line
            else:
                result_list = json_line["result_list"]
                json_lines = []
                accumulate = 0
                while True:
                    cur_result_list = []
                    for result in result_list:
                        if result["end"] - result["start"] > max_content_len:
                            logger.warning(
                                "result['end'] - result ['start'] exceeds max_content_len, which will result in no valid instance being returned"
                            )
                        if (
                            result["start"] + 1 <= max_content_len < result["end"]
                            and result["end"] - result["start"] <= max_content_len
                        ):
                            max_content_len = result["start"]
                            break

                    cur_content = content[:max_content_len]
                    res_content = content[max_content_len:]

                    while True:
                        if len(result_list) == 0:
                            break
                        elif result_list[0]["end"] <= max_content_len:
                            if result_list[0]["end"] > 0:
                                cur_result = result_list.pop(0)
                                cur_result_list.append(cur_result)
                            else:
                                cur_result_list = [result for result in result_list]
                                break
                        else:
                            break

                    json_line = {"content": cur_content, "result_list": cur_result_list, "prompt": prompt}
                    json_lines.append(json_line)

                    for result in result_list:
                        if result["end"] <= 0:
                            break
                        result["start"] -= max_content_len
                        result["end"] -= max_content_len
                    accumulate += max_content_len
                    max_content_len = max_seq_len - len(prompt) - 3
                    if len(res_content) == 0:
                        break
                    elif len(res_content) < max_content_len:
                        json_line = {"content": res_content, "result_list": result_list, "prompt": prompt}
                        json_lines.append(json_line)
                        break
                    else:
                        content = res_content

                for json_line in json_lines:
                    yield json_line


def unify_prompt_name(prompt):
    # The classification labels are shuffled during finetuning, so they need
    # to be unified during evaluation.
    if re.search(r"\[.*?\]$", prompt):
        prompt_prefix = prompt[: prompt.find("[", 1)]
        cls_options = re.search(r"\[.*?\]$", prompt).group()[1:-1].split(",")
        cls_options = sorted(list(set(cls_options)))
        cls_options = ",".join(cls_options)
        prompt = prompt_prefix + "[" + cls_options + "]"
        return prompt
    return prompt


def get_relation_type_dict(relation_data, schema_lang="ch"):
    def compare(a, b, schema_lang="ch"):
        if schema_lang == "ch":
            a = a[::-1]
            b = b[::-1]

        res = ""
        for i in range(min(len(a), len(b))):
            if a[i] == b[i]:
                res += a[i]
            else:
                break
        if res == "":
            return res
        if schema_lang == "ch" and res[::-1][0] == "的":
            return res[::-1][1:]
        elif schema_lang == "en" and res[-3:] == " of":
            return res[:-3]
        return ""

    relation_type_dict = {}
    added_list = []
    for i in range(len(relation_data)):
        added = False
        if relation_data[i][0] not in added_list:
            for j in range(i + 1, len(relation_data)):
                match = compare(relation_data[i][0], relation_data[j][0], schema_lang=schema_lang)
                if match != "":
                    match = unify_prompt_name(match)
                    if relation_data[i][0] not in added_list:
                        added_list.append(relation_data[i][0])
                        relation_type_dict.setdefault(match, []).append(relation_data[i][1])
                    added_list.append(relation_data[j][0])
                    relation_type_dict.setdefault(match, []).append(relation_data[j][1])
                    added = True
            if not added:
                added_list.append(relation_data[i][0])
                if schema_lang == "ch":
                    suffix = relation_data[i][0].rsplit("的", 1)[1]
                    suffix = unify_prompt_name(suffix)
                    relation_type = suffix
                else:
                    prefix = relation_data[i][0].split(" of ", 1)[0]
                    prefix = unify_prompt_name(prefix)
                    relation_type = prefix
                relation_type_dict.setdefault(relation_type, []).append(relation_data[i][1])
    return relation_type_dict


def add_entity_negative_example(examples, texts, prompts, label_set, negative_ratio):
    negative_examples = []
    positive_examples = []
    with tqdm(total=len(prompts)) as pbar:
        for i, prompt in enumerate(prompts):
            redundants = list(set(label_set) ^ set(prompt))
            redundants.sort()

            num_positive = len(examples[i])
            if num_positive != 0:
                actual_ratio = math.ceil(len(redundants) / num_positive)
            else:
                # Set num_positive to 1 for text without positive example
                num_positive, actual_ratio = 1, 0

            if actual_ratio <= negative_ratio or negative_ratio == -1:
                idxs = [k for k in range(len(redundants))]
            else:
                idxs = random.sample(range(0, len(redundants)), negative_ratio * num_positive)

            for idx in idxs:
                negative_result = {"content": texts[i], "result_list": [], "prompt": redundants[idx]}
                negative_examples.append(negative_result)
            positive_examples.extend(examples[i])
            pbar.update(1)
    return positive_examples, negative_examples


def add_relation_negative_example(redundants, text, num_positive, ratio):
    added_example = []
    rest_example = []

    if num_positive != 0:
        actual_ratio = math.ceil(len(redundants) / num_positive)
    else:
        # Set num_positive to 1 for text without positive example
        num_positive, actual_ratio = 1, 0

    all_idxs = [k for k in range(len(redundants))]
    if actual_ratio <= ratio or ratio == -1:
        idxs = all_idxs
        rest_idxs = []
    else:
        idxs = random.sample(range(0, len(redundants)), ratio * num_positive)
        rest_idxs = list(set(all_idxs) ^ set(idxs))

    for idx in idxs:
        negative_result = {"content": text, "result_list": [], "prompt": redundants[idx]}
        added_example.append(negative_result)

    for rest_idx in rest_idxs:
        negative_result = {"content": text, "result_list": [], "prompt": redundants[rest_idx]}
        rest_example.append(negative_result)

    return added_example, rest_example


def add_full_negative_example(examples, texts, relation_prompts, predicate_set, subject_goldens, schema_lang="ch"):
    with tqdm(total=len(relation_prompts)) as pbar:
        for i, relation_prompt in enumerate(relation_prompts):
            negative_sample = []
            for subject in subject_goldens[i]:
                for predicate in predicate_set:
                    # The relation prompt is constructed as follows:
                    # subject + "的" + predicate -> Chinese
                    # predicate + " of " + subject -> English
                    if schema_lang == "ch":
                        prompt = subject + "的" + predicate
                    else:
                        prompt = predicate + " of " + subject
                    if prompt not in relation_prompt:
                        negative_result = {"content": texts[i], "result_list": [], "prompt": prompt}
                        negative_sample.append(negative_result)
            examples[i].extend(negative_sample)
            pbar.update(1)
    return examples


def generate_cls_example(text, labels, prompt_prefix, options):
    random.shuffle(options)
    cls_options = ",".join(options)
    prompt = prompt_prefix + "[" + cls_options + "]"

    result_list = []
    example = {"content": text, "result_list": result_list, "prompt": prompt}
    for label in labels:
        start = prompt.rfind(label) - len(prompt) - 1
        end = start + len(label)
        result = {"text": label, "start": start, "end": end}
        example["result_list"].append(result)
    return example


def convert_cls_examples(raw_examples, prompt_prefix="情感倾向", options=["正向", "负向"]):
    """
    Convert labeled data export from doccano for classification task.
    """
    examples = []
    logger.info("Converting doccano data...")
    with tqdm(total=len(raw_examples)):
        for line in raw_examples:
            items = json.loads(line)
            # Compatible with doccano >= 1.6.2
            if "data" in items.keys():
                text, labels = items["data"], items["label"]
            else:
                text, labels = items["text"], items["label"]
            example = generate_cls_example(text, labels, prompt_prefix, options)
            examples.append(example)
    return examples


def convert_ext_examples(
    raw_examples,
    negative_ratio,
    prompt_prefix="情感倾向",
    options=["正向", "负向"],
    separator="##",
    is_train=True,
    schema_lang="ch",
):
    """
    Convert labeled data export from doccano for extraction and aspect-level classification task.
    """

    def _sep_cls_label(label, separator):
        label_list = label.split(separator)
        if len(label_list) == 1:
            return label_list[0], None
        return label_list[0], label_list[1:]

    texts = []
    entity_examples = []
    relation_examples = []
    entity_cls_examples = []
    entity_prompts = []
    relation_prompts = []
    entity_label_set = []
    entity_name_set = []
    predicate_set = []
    subject_goldens = []
    inverse_relation_list = []
    predicate_list = []

    logger.info("Converting doccano data...")
    with tqdm(total=len(raw_examples)) as pbar:
        for line in raw_examples:
            items = json.loads(line)
            entity_id = 0
            if "data" in items.keys():
                relation_mode = False
                if isinstance(items["label"], dict) and "entities" in items["label"].keys():
                    relation_mode = True
                text = items["data"]
                entities = []
                relations = []
                if not relation_mode:
                    # Export file in JSONL format which doccano < 1.7.0
                    # e.g. {"data": "", "label": [ [0, 2, "ORG"], ... ]}
                    for item in items["label"]:
                        entity = {"id": entity_id, "start_offset": item[0], "end_offset": item[1], "label": item[2]}
                        entities.append(entity)
                        entity_id += 1
                else:
                    # Export file in JSONL format for relation labeling task which doccano < 1.7.0
                    # e.g. {"data": "", "label": {"relations": [ {"id": 0, "start_offset": 0, "end_offset": 6, "label": "ORG"}, ... ], "entities": [ {"id": 0, "from_id": 0, "to_id": 1, "type": "foundedAt"}, ... ]}}
                    entities.extend([entity for entity in items["label"]["entities"]])
                    if "relations" in items["label"].keys():
                        relations.extend([relation for relation in items["label"]["relations"]])
            else:
                # Export file in JSONL format which doccano >= 1.7.0
                # e.g. {"text": "", "label": [ [0, 2, "ORG"], ... ]}
                if "label" in items.keys():
                    text = items["text"]
                    entities = []
                    for item in items["label"]:
                        entity = {"id": entity_id, "start_offset": item[0], "end_offset": item[1], "label": item[2]}
                        entities.append(entity)
                        entity_id += 1
                    relations = []
                else:
                    # Export file in JSONL (relation) format
                    # e.g. {"text": "", "relations": [ {"id": 0, "start_offset": 0, "end_offset": 6, "label": "ORG"}, ... ], "entities": [ {"id": 0, "from_id": 0, "to_id": 1, "type": "foundedAt"}, ... ]}
                    text, relations, entities = items["text"], items["relations"], items["entities"]
            texts.append(text)

            entity_example = []
            entity_prompt = []
            entity_example_map = {}
            entity_map = {}  # id to entity name
            for entity in entities:
                entity_name = text[entity["start_offset"] : entity["end_offset"]]
                entity_map[entity["id"]] = {
                    "name": entity_name,
                    "start": entity["start_offset"],
                    "end": entity["end_offset"],
                }

                entity_label, entity_cls_label = _sep_cls_label(entity["label"], separator)

                # Define the prompt prefix for entity-level classification
                # xxx + "的" + 情感倾向 -> Chinese
                # Sentiment classification + " of " + xxx -> English
                if schema_lang == "ch":
                    entity_cls_prompt_prefix = entity_name + "的" + prompt_prefix
                else:
                    entity_cls_prompt_prefix = prompt_prefix + " of " + entity_name
                if entity_cls_label is not None:
                    entity_cls_example = generate_cls_example(
                        text, entity_cls_label, entity_cls_prompt_prefix, options
                    )

                    entity_cls_examples.append(entity_cls_example)

                result = {"text": entity_name, "start": entity["start_offset"], "end": entity["end_offset"]}
                if entity_label not in entity_example_map.keys():
                    entity_example_map[entity_label] = {
                        "content": text,
                        "result_list": [result],
                        "prompt": entity_label,
                    }
                else:
                    entity_example_map[entity_label]["result_list"].append(result)

                if entity_label not in entity_label_set:
                    entity_label_set.append(entity_label)
                if entity_name not in entity_name_set:
                    entity_name_set.append(entity_name)
                entity_prompt.append(entity_label)

            for v in entity_example_map.values():
                entity_example.append(v)

            entity_examples.append(entity_example)
            entity_prompts.append(entity_prompt)

            subject_golden = []  # Golden entity inputs
            relation_example = []
            relation_prompt = []
            relation_example_map = {}
            inverse_relation = []
            predicates = []
            for relation in relations:
                predicate = relation["type"]
                subject_id = relation["from_id"]
                object_id = relation["to_id"]
                # The relation prompt is constructed as follows:
                # subject + "的" + predicate -> Chinese
                # predicate + " of " + subject -> English
                if schema_lang == "ch":
                    prompt = entity_map[subject_id]["name"] + "的" + predicate
                    inverse_negative = entity_map[object_id]["name"] + "的" + predicate
                else:
                    prompt = predicate + " of " + entity_map[subject_id]["name"]
                    inverse_negative = predicate + " of " + entity_map[object_id]["name"]

                if entity_map[subject_id]["name"] not in subject_golden:
                    subject_golden.append(entity_map[subject_id]["name"])
                result = {
                    "text": entity_map[object_id]["name"],
                    "start": entity_map[object_id]["start"],
                    "end": entity_map[object_id]["end"],
                }

                inverse_relation.append(inverse_negative)
                predicates.append(predicate)

                if prompt not in relation_example_map.keys():
                    relation_example_map[prompt] = {"content": text, "result_list": [result], "prompt": prompt}
                else:
                    relation_example_map[prompt]["result_list"].append(result)

                if predicate not in predicate_set:
                    predicate_set.append(predicate)
                relation_prompt.append(prompt)

            for v in relation_example_map.values():
                relation_example.append(v)

            relation_examples.append(relation_example)
            relation_prompts.append(relation_prompt)
            subject_goldens.append(subject_golden)
            inverse_relation_list.append(inverse_relation)
            predicate_list.append(predicates)
            pbar.update(1)

    logger.info("Adding negative samples for first stage prompt...")
    positive_examples, negative_examples = add_entity_negative_example(
        entity_examples, texts, entity_prompts, entity_label_set, negative_ratio
    )
    if len(positive_examples) == 0:
        all_entity_examples = []
    else:
        all_entity_examples = positive_examples + negative_examples

    all_relation_examples = []
    if len(predicate_set) != 0:
        logger.info("Adding negative samples for second stage prompt...")
        if is_train:

            positive_examples = []
            negative_examples = []
            per_n_ratio = negative_ratio // 3

            with tqdm(total=len(texts)) as pbar:
                for i, text in enumerate(texts):
                    negative_example = []
                    collects = []
                    num_positive = len(relation_examples[i])

                    # 1. inverse_relation_list
                    redundants1 = inverse_relation_list[i]

                    # 2. entity_name_set ^ subject_goldens[i]
                    redundants2 = []
                    if len(predicate_list[i]) != 0:
                        nonentity_list = list(set(entity_name_set) ^ set(subject_goldens[i]))
                        nonentity_list.sort()

                        if schema_lang == "ch":
                            redundants2 = [
                                nonentity + "的" + predicate_list[i][random.randrange(len(predicate_list[i]))]
                                for nonentity in nonentity_list
                            ]
                        else:
                            redundants2 = [
                                predicate_list[i][random.randrange(len(predicate_list[i]))] + " of " + nonentity
                                for nonentity in nonentity_list
                            ]

                    # 3. entity_label_set ^ entity_prompts[i]
                    redundants3 = []
                    if len(subject_goldens[i]) != 0:
                        non_ent_label_list = list(set(entity_label_set) ^ set(entity_prompts[i]))
                        non_ent_label_list.sort()

                        if schema_lang == "ch":
                            redundants3 = [
                                subject_goldens[i][random.randrange(len(subject_goldens[i]))] + "的" + non_ent_label
                                for non_ent_label in non_ent_label_list
                            ]
                        else:
                            redundants3 = [
                                non_ent_label + " of " + subject_goldens[i][random.randrange(len(subject_goldens[i]))]
                                for non_ent_label in non_ent_label_list
                            ]

                    redundants_list = [redundants1, redundants2, redundants3]

                    for redundants in redundants_list:
                        added, rest = add_relation_negative_example(
                            redundants,
                            texts[i],
                            num_positive,
                            per_n_ratio,
                        )
                        negative_example.extend(added)
                        collects.extend(rest)

                    num_sup = num_positive * negative_ratio - len(negative_example)
                    if num_sup > 0 and collects:
                        if num_sup > len(collects):
                            idxs = [k for k in range(len(collects))]
                        else:
                            idxs = random.sample(range(0, len(collects)), num_sup)
                        for idx in idxs:
                            negative_example.append(collects[idx])

                    positive_examples.extend(relation_examples[i])
                    negative_examples.extend(negative_example)
                    pbar.update(1)
            all_relation_examples = positive_examples + negative_examples
        else:
            relation_examples = add_full_negative_example(
                relation_examples, texts, relation_prompts, predicate_set, subject_goldens, schema_lang=schema_lang
            )
            all_relation_examples = [r for relation_example in relation_examples for r in relation_example]
    return all_entity_examples, all_relation_examples, entity_cls_examples


def get_dynamic_max_length(examples, default_max_length: int, dynamic_max_length: List[int]) -> int:
    """get max_length by examples which you can change it by examples in batch"""
    cur_length = len(examples[0]["input_ids"])
    max_length = default_max_length
    for max_length_option in sorted(dynamic_max_length):
        if cur_length <= max_length_option:
            max_length = max_length_option
            break
    return max_length


def convert_example(
    example, tokenizer, max_seq_len, multilingual=False, dynamic_max_length: Optional[List[int]] = None
):
    """
    example: {
        title
        prompt
        content
        result_list
    }
    """
    if dynamic_max_length is not None:
        temp_encoded_inputs = tokenizer(
            text=[example["prompt"]],
            text_pair=[example["content"]],
            truncation=True,
            max_seq_len=max_seq_len,
            return_attention_mask=True,
            return_position_ids=True,
            return_dict=False,
            return_offsets_mapping=True,
        )
        max_length = get_dynamic_max_length(
            examples=temp_encoded_inputs, default_max_length=max_seq_len, dynamic_max_length=dynamic_max_length
        )
        # always pad to max_length
        encoded_inputs = tokenizer(
            text=[example["prompt"]],
            text_pair=[example["content"]],
            truncation=True,
            max_seq_len=max_length,
            pad_to_max_seq_len=True,
            return_attention_mask=True,
            return_position_ids=True,
            return_dict=False,
            return_offsets_mapping=True,
        )
        start_ids = [0.0 for x in range(max_length)]
        end_ids = [0.0 for x in range(max_length)]
    else:
        encoded_inputs = tokenizer(
            text=[example["prompt"]],
            text_pair=[example["content"]],
            truncation=True,
            max_seq_len=max_seq_len,
            pad_to_max_seq_len=True,
            return_attention_mask=True,
            return_position_ids=True,
            return_dict=False,
            return_offsets_mapping=True,
        )
        start_ids = [0.0 for x in range(max_seq_len)]
        end_ids = [0.0 for x in range(max_seq_len)]

    encoded_inputs = encoded_inputs[0]
    offset_mapping = [list(x) for x in encoded_inputs["offset_mapping"]]
    bias = 0
    for index in range(1, len(offset_mapping)):
        mapping = offset_mapping[index]
        if mapping[0] == 0 and mapping[1] == 0 and bias == 0:
            bias = offset_mapping[index - 1][1] + 1  # Includes [SEP] token
        if mapping[0] == 0 and mapping[1] == 0:
            continue
        offset_mapping[index][0] += bias
        offset_mapping[index][1] += bias
    for item in example["result_list"]:
        start = map_offset(item["start"] + bias, offset_mapping)
        end = map_offset(item["end"] - 1 + bias, offset_mapping)
        start_ids[start] = 1.0
        end_ids[end] = 1.0
    if multilingual:
        tokenized_output = {
            "input_ids": encoded_inputs["input_ids"],
            "position_ids": encoded_inputs["position_ids"],
            "start_positions": start_ids,
            "end_positions": end_ids,
        }
    else:
        tokenized_output = {
            "input_ids": encoded_inputs["input_ids"],
            "token_type_ids": encoded_inputs["token_type_ids"],
            "position_ids": encoded_inputs["position_ids"],
            "attention_mask": encoded_inputs["attention_mask"],
            "start_positions": start_ids,
            "end_positions": end_ids,
        }
    return tokenized_output

训练

# Copyright (c) 2022 PaddlePaddle Authors. 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.

import os
from dataclasses import dataclass, field
from functools import partial
from typing import List, Optional

import paddle
from utils import convert_example, reader

from paddlenlp.data import DataCollatorWithPadding
from paddlenlp.datasets import load_dataset
from paddlenlp.metrics import SpanEvaluator
from paddlenlp.trainer import (
    CompressionArguments,
    PdArgumentParser,
    Trainer,
    get_last_checkpoint,
)
from paddlenlp.transformers import UIE, UIEM, AutoTokenizer, export_model
from paddlenlp.utils.log import logger

@dataclass
class DataArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    Using `PdArgumentParser` we can turn this class into argparse arguments to be able to
    specify them on the command line.
    """

    train_path: str = field(
        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )

    dev_path: str = field(
        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
    )

    max_seq_length: Optional[int] = field(
        default=512,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )

    dynamic_max_length: Optional[List[int]] = field(
        default=None,
        metadata={"help": "dynamic max length from batch, it can be array of length, eg: 16 32 64 128"},
    )


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: Optional[str] = field(
        default="uie-base",
        metadata={
            "help": "Path to pretrained model, such as 'uie-base', 'uie-tiny', "
            "'uie-medium', 'uie-mini', 'uie-micro', 'uie-nano', 'uie-base-en', "
            "'uie-m-base', 'uie-m-large', or finetuned model path."
        },
    )
    export_model_dir: Optional[str] = field(
        default=None,
        metadata={"help": "Path to directory to store the exported inference model."},
    )
    multilingual: bool = field(default=False, metadata={"help": "Whether the model is a multilingual model."})


def main():
    parser = PdArgumentParser((ModelArguments, DataArguments, CompressionArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    if model_args.model_name_or_path in ["uie-m-base", "uie-m-large"]:
        model_args.multilingual = True

    # Log model and data config
    training_args.print_config(model_args, "Model")
    training_args.print_config(data_args, "Data")

    paddle.set_device(training_args.device)

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, world_size: {training_args.world_size}, "
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )

    # 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."
            )

    tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
    if model_args.multilingual:
        model = UIEM.from_pretrained(model_args.model_name_or_path)
    else:
        model = UIE.from_pretrained(model_args.model_name_or_path)

    train_ds = load_dataset(reader, data_path=data_args.train_path, max_seq_len=data_args.max_seq_length, lazy=False)
    dev_ds = load_dataset(reader, data_path=data_args.dev_path, max_seq_len=data_args.max_seq_length, lazy=False)

    trans_fn = partial(
        convert_example,
        tokenizer=tokenizer,
        max_seq_len=data_args.max_seq_length,
        multilingual=model_args.multilingual,
        dynamic_max_length=data_args.dynamic_max_length,
    )

    train_ds = train_ds.map(trans_fn)
    dev_ds = dev_ds.map(trans_fn)

    if training_args.device == "npu":
        data_collator = DataCollatorWithPadding(tokenizer, padding="longest")
    else:
        data_collator = DataCollatorWithPadding(tokenizer)

    criterion = paddle.nn.BCELoss()

    def uie_loss_func(outputs, labels):
        start_ids, end_ids = labels
        start_prob, end_prob = outputs
        start_ids = paddle.cast(start_ids, "float32")
        end_ids = paddle.cast(end_ids, "float32")
        loss_start = criterion(start_prob, start_ids)
        loss_end = criterion(end_prob, end_ids)
        loss = (loss_start + loss_end) / 2.0
        return loss

    def compute_metrics(p):
        metric = SpanEvaluator()
        start_prob, end_prob = p.predictions
        start_ids, end_ids = p.label_ids
        metric.reset()

        num_correct, num_infer, num_label = metric.compute(start_prob, end_prob, start_ids, end_ids)
        metric.update(num_correct, num_infer, num_label)
        precision, recall, f1 = metric.accumulate()
        metric.reset()

        return {"precision": precision, "recall": recall, "f1": f1}

    trainer = Trainer(
        model=model,
        criterion=uie_loss_func,
        args=training_args,
        data_collator=data_collator,
        train_dataset=train_ds if training_args.do_train or training_args.do_compress else None,
        eval_dataset=dev_ds if training_args.do_eval or training_args.do_compress else None,
        tokenizer=tokenizer,
        compute_metrics=compute_metrics,
    )

    trainer.optimizer = paddle.optimizer.AdamW(
        learning_rate=training_args.learning_rate, parameters=model.parameters()
    )
    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

    # Training
    if training_args.do_train:
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        metrics = train_result.metrics
        trainer.save_model()
        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluate and tests model
    if training_args.do_eval:
        eval_metrics = trainer.evaluate()
        trainer.log_metrics("eval", eval_metrics)

    # export inference model
    if training_args.do_export:
        # You can also load from certain checkpoint
        # trainer.load_state_dict_from_checkpoint("/path/to/checkpoint/")
        if training_args.device == "npu":
            # npu will transform int64 to int32 for internal calculation.
            # To reduce useless transformation, we feed int32 inputs.
            input_spec_dtype = "int32"
        else:
            input_spec_dtype = "int64"
        if model_args.multilingual:
            input_spec = [
                paddle.static.InputSpec(shape=[None, None], dtype=input_spec_dtype, name="input_ids"),
                paddle.static.InputSpec(shape=[None, None], dtype=input_spec_dtype, name="position_ids"),
            ]
        else:
            input_spec = [
                paddle.static.InputSpec(shape=[None, None], dtype=input_spec_dtype, name="input_ids"),
                paddle.static.InputSpec(shape=[None, None], dtype=input_spec_dtype, name="token_type_ids"),
                paddle.static.InputSpec(shape=[None, None], dtype=input_spec_dtype, name="position_ids"),
                paddle.static.InputSpec(shape=[None, None], dtype=input_spec_dtype, name="attention_mask"),
            ]
        if model_args.export_model_dir is None:
            model_args.export_model_dir = os.path.join(training_args.output_dir, "export")
        export_model(model=trainer.model, input_spec=input_spec, path=model_args.export_model_dir)
        trainer.tokenizer.save_pretrained(model_args.export_model_dir)

    if training_args.do_compress:

        @paddle.no_grad()
        def custom_evaluate(self, model, data_loader):
            metric = SpanEvaluator()
            model.eval()
            metric.reset()
            for batch in data_loader:
                if model_args.multilingual:
                    logits = model(input_ids=batch["input_ids"], position_ids=batch["position_ids"])
                else:
                    logits = model(
                        input_ids=batch["input_ids"],
                        token_type_ids=batch["token_type_ids"],
                        position_ids=batch["position_ids"],
                        attention_mask=batch["attention_mask"],
                    )
                start_prob, end_prob = logits
                start_ids, end_ids = batch["start_positions"], batch["end_positions"]
                num_correct, num_infer, num_label = metric.compute(start_prob, end_prob, start_ids, end_ids)
                metric.update(num_correct, num_infer, num_label)
            precision, recall, f1 = metric.accumulate()
            logger.info("f1: %s, precision: %s, recall: %s" % (f1, precision, f1))
            model.train()
            return f1

        trainer.compress(custom_evaluate=custom_evaluate)


if __name__ == "__main__":
    main()

## CPU训练
python finetune.py --device cpu --logging_steps 10 --save_steps 100 --eval_steps 100 --seed 42 --model_name_or_path uie-base --output_dir model --train_path data/train.txt --dev_path data/dev.txt --max_seq_length 512 --per_device_eval_batch_size 16 --per_device_train_batch_size  16 --num_train_epochs 20 --learning_rate 1e-5 --label_names "start_positions" "end_positions" --do_train --do_eval --do_export --export_model_dir model --overwrite_output_dir --disable_tqdm True --metric_for_best_model eval_f1 --load_best_model_at_end  True --save_total_limit 1

## GPU多卡训练
python3 -u -m paddle.distributed.launch --gpus "0,1" finetune.py --device gpu --logging_steps 10 --save_steps 100 --eval_steps 100 --seed 42 --model_name_or_path uie-base --output_dir model --train_path data/train.txt --dev_path data/dev.txt --max_seq_length 512 --per_device_eval_batch_size 7 --per_device_train_batch_size 7 --num_train_epochs 20 --learning_rate 1e-5 --label_names "start_positions" "end_positions" --do_train --do_eval --do_export --export_model_dir model --overwrite_output_dir --disable_tqdm True --metric_for_best_model eval_f1 --load_best_model_at_end  True --save_total_limit 1

PDF或图片文档内容识别、关系抽取

模型部署预测

import gradio as gr
from paddlenlp import Taskflow

schema = ['时间', '选手', '赛事名称']
ie = Taskflow('information_extraction', schema=schema, task_path='model')


# UGC: Define the inference fn() for your models
def model_inference(schema, text):

    ie.set_schema(eval(schema))
    res = ie(text)
    json_out = {"text": text, "result": res}
    return json_out


def clear_all():
    return None, None, None

with gr.Blocks() as demo:
    gr.Markdown("关系抽取")

    with gr.Column(scale=1, min_width=100):
        schema = gr.Textbox(
            placeholder="['时间', '选手', '赛事名称']",
            label="输入结构:",
            lines=2)
        text = gr.Textbox(
            placeholder="2月8日上午北京冬奥会自由式滑雪女子大跳台决赛中中国选手谷爱凌以188.25分获得金牌！",
            label="输入内容:",
            lines=2)

        with gr.Row():
            btn1 = gr.Button("Clear")
            btn2 = gr.Button("Submit")
        json_out = gr.JSON(label="Information Extraction Output")

    btn1.click(fn=clear_all, inputs=None, outputs=[schema, text, json_out])
    btn2.click(fn=model_inference, inputs=[schema, text], outputs=[json_out])
    gr.Button.style(1)

demo.launch(server_name='192.168.1.111', share=True, server_port=7006)

## 裁定书  ['标题',{'申请执行人': ['姓名', '生于', '身份证号']},{'被执行人': ['姓名', '生于', '身份证号']},'公积金']

## 调解书 ['法院',{'原告': ['姓名', '生于', '身份证号']},{'被告': ['公司']},'公积金']

## 测试 ['标题','诉讼案号','执行案号','公积金',{'原告': ['姓名', '单位', '生日', '身份证号']},{'被告': ['姓名', '单位', '生日', '身份证号']}]