VictorYeste/deberta-based-human-value-stance-detection

Description

The Human Value Detection at CLEF 2024 task consists of two sub-tasks: the first is to detect the presence or absence of each of these 19 values, while the second is to detect whether the value is attained or constrained.

Our system introduces a cascade model approach for the detection and stance classification of the predefined set of human values. It consists of two subsystems: one for detecting the presence of each human value and another for establishing the stance (if the sentence attains or constrains) of each human value. Each subsystem is designed and fine-tuned separately using a DeBERTa model as base.

• Subsystem 1: Its primary function is to identify the presence of human values within sentences. By combining the 'attained' and 'constrained' labels to indicate an overall presence, it streamlines the multi-label classification task, simplifying it to a binary classification for each of the 19 human values (presence vs. absence).
• Subsystem 2: it receives the outputs of subsystem 1 and classifies the stance towards each present human value in a binary classification (attained vs. constrained). This system transforms the sentences dataset into premise-hypothesis pairs, where each sentence is the premise, a value is the hypothesis, and the “attained” and “constrained” labels are the stance.

Given that subsystem 1 focuses on detecting the presence of human values in the text, and subsystem 2 focuses on the stances towards each detected human value, this cascade model approach improves the granularity of text classification.

This model is the responsible of the Subsystem 2 and accomplishes the second sub-task.

How to use

from transformers import pipeline

model = "VictorYeste/deberta-based-human-value-stance-detection"
tokenizer = "VictorYeste/deberta-based-human-value-stance-detection"

stance_detection = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=None)

stance_detection("We are not humble. Humility")

This returns the following:

[[{'label': 'constrained', 'score': 0.7598766088485718},
  {'label': 'attained', 'score': 0.24012333154678345}]]