Model Card for deberta-v3-large-self-disclosure-detection

The model is used to detect self-disclosures (personal information) in a sentence. It is a multi-class token classification task like NER in IOB2 format. For example "I am 22 years old and ..." has labels of "["B-Age", "I-Age", "I-Age", "I-Age", "I-Age", "O", ...]"

The model is able to detect the following 17 categores: "Age", "Age_Gender", "Appearance", "Education", "Family", "Finance", "Gender", "Health", "Husband_BF", "Location", "Mental_Health", "Occupation", "Pet", "Race_Nationality", "Relationship_Status", "Sexual_Orientation", "Wife_GF".

For more details, please read the paper: Reducing Privacy Risks in Online Self-Disclosures with Language Models .

Accessing this model implies automatic agreement to the following guidelines:

1. Only use the model for research purposes.
2. No redistribution without the author's agreement.
3. Any derivative works created using this model must acknowledge the original author.

Model Description

• Model type: A finetuned model that can detect self-disclosures in 17 categories.
• Language(s) (NLP): English
• License: Creative Commons Attribution-NonCommercial
• Finetuned from model: microsoft/deberta-v3-large

Example Code

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)

Evaluation

The model achieves 65.71 partial span F1, better than prompting GPT-4 (57.68 F1). For detailed performance per category, see paper.

Citation

@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}
}