Animised/nli-cdv2

Animised NLI Contradiction Detector v2

prajjwal1/bert-small (29M) trained directly on hard labels
with a 3:1 imbalanced dataset to prevent contradiction bias.

Why v2?

The v1 model (distilled from teacher soft labels on a balanced dataset)
showed a strong contradiction bias — predicting contradiction even
in clearly entailing cases.

v2 fixes this by training on a deliberately imbalanced dataset where
entailment+neutral outnumber contradiction 3:1. This makes the model
conservative about predicting contradiction — it requires stronger
evidence before flagging something as inconsistent.

Results

Metric	Value
Accuracy	0.8120 (81.20%)
Loss	0.480365
Epochs	4

Labels

0 = entailment | 1 = neutral | 2 = contradiction

Usage

from transformers import pipeline

clf = pipeline("text-classification", model="Animised/nli-cdv2")

clf(
    "Rem was raised by her mother [SEP] Rem's mum taught her to cook soba.",
    top_k=None
)

Purpose

Character fact consistency checker for the
Animised project —
detects when generated dialogue contradicts a character's bible.

Training details

• Base model : prajjwal1/bert-small (29M params)
• Dataset : Animised/nli-v2
• Data ratio : 3:1 (entailment+neutral : contradiction)
• Loss : CrossEntropyLoss (hard labels, no distillation)
• Epochs : 4
• Batch size : 512
• Max length : 256
• LR : 4e-05
• GPUs : 2

vs v1

Feature	v1	v2
Training	Distillation (soft labels)	Direct (hard labels)
Data balance	1:1:1	3:1 (E+N:C)
Contradiction	Trigger-happy (~82% false)	Conservative
Accuracy	79.6%	81.20%