Figure 1. Sentence-length distributions for contexts, positive, negative, and neutral arguments across datasets.
Why it matters Balanced lengths & human-verified neutrals stop models from “cheating” on superficial cues and keep the focus on genuine oppositional content.
Opposite-Score
Efficient embeddings and scoring mechanism for detecting contrasting or opposite relationships in text, based on a given context.
Opposite-Score is designed to generate embeddings and compute the opposite-score, which quantifies the degree of contrast or opposition between two textual outputs within the same context. This package is particularly useful in scenarios like debates, legal reasoning, and causal analysis where contrasting perspectives need to be evaluated based on shared input.
Features
Opposite-Score Calculation: Computes a numerical score representing how opposite two texts are, conditioned on a shared context.
Opposite-Aware Embeddings: Generates embeddings optimized for contrasting textual relationships.
Easy to Use: Only a few lines of code to get sentence/token-level embeddings and calculate opposite scores.
Automatic Model Download: The first initialization automatically downloads and installs the pre-trained Opposite-Score model.
Installation
Install Opposite-Score via pip:
pip install opposite-score==0.0.1
💡 Usage Examples
For Opposite Scores of two sentence conditioned on the context.
python
12from oppositescore.model.dichotomye import DichotomyE
34# Example inputs5context =["A company launches a revolutionary product."]6sentence1 =["Competitors quickly release similar products, reducing the company's advantage."]7sentence2 =["The company gains a significant advantage due to its unique product."]89# Initialize the model10opposite_scorer = DichotomyE.from_pretrained('shaobocui/opposite-score-debate-bert', pooling_strategy='cls').cuda()1112# Calculate opposite-score (using cosine similarity as an example)13opposite_score = opposite_scorer.calculate_opposite_score(ctx=context, sent1=sentence1, sent2=sentence2)14print('Opposite Score:', opposite_score)15# Output: Opposite Score: 1.5123178
🛡 Responsible Usage
This software is released for research and educational purposes only. It is intended to support studies on argument contrast, causal reasoning, and sentence embeddings.
Please ensure proper attribution when using the code, models, or datasets in publications or derivative work. Commercial use are expected to contact authors for explicit permission.
For questions or collaborations, feel free to contact the authors.
📊 Leaderboard: Dichotomy Quantification
Model
Debate (DCF ↑)
Debate (Angle ↑)
NLI (DCF ↑)
NLI (Angle ↑)
Causal (DCF ↑)
Causal (Angle ↑)
InferSent-GloVe
36.19
1.58
23.11
0.39
26.71
0.44
InferSent-fastText
42.02
4.56
27.66
1.42
32.36
1.44
USE
16.53
3.31
18.07
1.01
13.54
0.46
BERT
31.37
0.18
11.99
0.25
27.17
0.26
CoSENT
38.49
0.64
26.86
0.28
30.07
0.14
SBERT
31.61
1.50
22.89
0.64
22.68
0.43
SimCSE (BERT)
30.59
2.78
13.91
0.93
25.15
1.30
AoE (BERT)
26.27
0.48
24.02
0.10
30.09
0.11
RoBERTa
43.61
0.00
12.60
0.00
24.06
0.00
SimCSE (RoBERTa)
30.84
2.42
12.78
0.64
27.01
1.28
LLaMA-2 (7B)
30.46
16.99
21.25
8.65
32.80
5.67
LLaMA-2 (13B)
47.42
11.24
30.27
4.59
34.05
2.56
AoE (7B)
38.92
14.85
20.01
8.22
27.20
4.03
AoE (13B)
44.88
9.73
28.72
3.20
30.89
1.58
LLaMA-3.1 (8B)
39.81
10.86
21.70
5.33
26.38
2.67
LLaMA-3.1 (70B)
34.47
13.74
15.95
6.83
25.23
3.84
Ours (BERT)
46.97
30.66
41.72
3.25
67.59
20.69
Ours (RoBERTa)
55.93
83.67
47.27
0.63
76.55
5.06
📌 Both metrics benefit from higher values: better classification and stronger geometric contrast.
