---
language: en
tags:
  - nli
  - contradiction-detection
  - animised
  - bert
license: apache-2.0
---

# 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
```python
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](https://huggingface.co/Animised) project —  
detects when generated dialogue contradicts a character's bible.

## Training details
- Base model  : `prajjwal1/bert-small` (29M params)
- Dataset     : [Animised/nli-v2](https://huggingface.co/datasets/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%         |