| --- |
| 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 |
| ```python |
| 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](https://huggingface.co/Animised) project. |
|
|
| ## Training details |
| - Base model : `prajjwal1/bert-medium` (41M params) |
| - Dataset : [Animised/nli-v3](https://huggingface.co/datasets/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 |
|
|
