Deberta V3 Large Self Disclosure Detection

🚀 deberta-v3-large-自我披露检测

该模型用于检测句子中的自我披露信息（个人信息），这是一个多类别标记分类任务，类似于采用IOB2格式的命名实体识别（NER）。它能为保护个人隐私、识别敏感信息提供有力支持。

🚀 快速开始

此模型用于检测句子中的自我披露信息（个人信息），是一个多类别标记分类任务，类似采用IOB2格式的NER。例如，句子 "I am 22 years old and ..." 的标签为 "["B - Age", "I - Age", "I - Age", "I - Age", "I - Age", "O", ...]" 。

该模型能够检测以下17个类别："Age"（年龄）、"Age_Gender"（年龄与性别）、"Appearance"（外貌）、"Education"（教育程度）、"Family"（家庭情况）、"Finance"（财务状况）、"Gender"（性别）、"Health"（健康状况）、"Husband_BF"（丈夫/男友）、"Location"（地理位置）、"Mental_Health"（心理健康）、"Occupation"（职业）、"Pet"（宠物）、"Race_Nationality"（种族/国籍）、"Relationship_Status"（感情状况）、"Sexual_Orientation"（性取向）、"Wife_GF"（妻子/女友）。

如需更多详细信息，请阅读论文：Reducing Privacy Risks in Online Self - Disclosures with Language Models 。

使用此模型意味着自动同意以下准则：

1. 仅将模型用于研究目的。
2. 未经作者同意，不得重新分发。
3. 任何使用此模型创建的衍生作品必须承认原作者。

✨ 主要特性

• 能够精准检测句子中17个类别的自我披露信息。
• 在相关评估指标上表现出色，部分跨度F1值达到65.71，优于提示GPT - 4（F1值为57.68）。

📦 安装指南

文档中未提及具体安装步骤，故跳过该章节。

💻 使用示例

基础用法

import torch
from torch.utils.data import DataLoader, Dataset

import datasets
from datasets import ClassLabel, load_dataset

from transformers import AutoModelForTokenClassification, AutoTokenizer, AutoConfig, DataCollatorForTokenClassification

model_path = "douy/deberta-v3-large-self-disclosure-detection"

config = AutoConfig.from_pretrained(model_path,)

tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True,)

model = AutoModelForTokenClassification.from_pretrained(model_path,config=config,device_map="cuda:0").eval()

label2id = config.label2id
id2label = config.id2label


def tokenize_and_align_labels(words):
    tokenized_inputs = tokenizer(
                words,
                padding=False,
                is_split_into_words=True,
            )

    # we use ("O") for all the labels
    word_ids = tokenized_inputs.word_ids(0)
    previous_word_idx = None
    label_ids = []
    for word_idx in word_ids:
        # Special tokens have a word id that is None. We set the label to -100 so they are automatically
        # ignored in the loss function.
        if word_idx is None:
            label_ids.append(-100)
        # We set the label for the first token of each word.
        elif word_idx != previous_word_idx:
            label_ids.append(label2id["O"])
        # For the other tokens in a word, we set the label to -100
        else:
            label_ids.append(-100)
        previous_word_idx = word_idx
    tokenized_inputs["labels"] = label_ids
    return tokenized_inputs

class DisclosureDataset(Dataset):
    def __init__(self, inputs, tokenizer, tokenize_and_align_labels_function):
        self.inputs = inputs
        self.tokenizer = tokenizer
        self.tokenize_and_align_labels_function = tokenize_and_align_labels_function

    def __len__(self):
        return len(self.inputs)

    def __getitem__(self, idx):
        words = self.inputs[idx]
        tokenized_inputs = self.tokenize_and_align_labels_function(words)
        return tokenized_inputs
    
    
sentences = [
    "I am a 23-year-old who is currently going through the last leg of undergraduate school.",
    "My husband and I live in US.",
]

inputs = [sentence.split() for sentence in sentences]

data_collator = DataCollatorForTokenClassification(tokenizer)

dataset = DisclosureDataset(inputs, tokenizer, tokenize_and_align_labels)

dataloader = DataLoader(dataset, collate_fn=data_collator, batch_size=2)

total_predictions = []
for step, batch in enumerate(dataloader):
    batch = {k: v.to(model.device) for k, v in batch.items()}
    with torch.inference_mode():
        outputs = model(**batch)
    predictions = outputs.logits.argmax(-1)
    labels = batch["labels"]

    predictions = predictions.cpu().tolist()
    labels = labels.cpu().tolist()

    true_predictions = []
    for i, label in enumerate(labels):
        true_pred = []
        for j, m in enumerate(label):
            if m != -100:
                true_pred.append(id2label[predictions[i][j]])
        true_predictions.append(true_pred)
    total_predictions.extend(true_predictions)
    

for word, pred in zip(inputs, total_predictions):
    for w, p in zip(word, pred):
        print(w, p)

📚 详细文档

模型描述

评估

该模型的部分跨度F1值达到65.71，优于提示GPT - 4（F1值为57.68）。有关每个类别的详细性能，请参阅论文。

🔧 技术细节

文档中未提及具体技术细节内容，故跳过该章节。

📄 许可证

该模型使用的许可证为知识共享署名 - 非商业性使用许可协议（Creative Commons Attribution - NonCommercial）。

📚 引用

@article{dou2023reducing,
  title={Reducing Privacy Risks in Online Self-Disclosures with Language Models},
  author={Dou, Yao and Krsek, Isadora and Naous, Tarek and Kabra, Anubha and Das, Sauvik and Ritter, Alan and Xu, Wei},
  journal={arXiv preprint arXiv:2311.09538},
  year={2023}
}