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 .
To get access to the model, you need to send email to douy@gatech.edu and agree with the following ethical guidelines:
- Only use the model for research purposes.
- No redistribution without the author's agreement.
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}
}
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microsoft/deberta-v3-large