metadata
language: en
tags:
- nli
- contradiction-detection
- animised
- bert
license: apache-2.0
Animised NLI Contradiction Detector v3
prajjwal1/bert-medium (41M) trained directly on hard labels
with a 3:1 imbalanced dataset to prevent contradiction bias.
Why v3?
Upgrade from bert-small to bert-medium for stronger performance,
while keeping the conservative contradiction policy from v2.
Results
| Metric | Value |
|---|---|
| Accuracy | 0.8636 (86.36%) |
| Loss | 0.366860 |
| Epochs | 4 |
Labels
0 = entailment | 1 = neutral | 2 = contradiction
Usage
from transformers import pipeline
clf = pipeline("text-classification", model="Animised/nli-cdv3")
clf("Premise [SEP] Hypothesis", top_k=None)
Purpose
Character fact consistency checker for the
Animised project.
Training details
- Base model :
prajjwal1/bert-medium(41M params) - Dataset : Animised/nli-v3
- Data ratio : 3:1 (entailment+neutral : contradiction)
- Loss : CrossEntropyLoss (hard labels)
- Epochs : 4
- Batch size : 384
- Max length : 256
- LR : 4e-05
- GPUs : 2