README.md

---
base_model: cross-encoder/nli-deberta-v3-xsmall
library_name: transformers.js
license: apache-2.0
language: en
pipeline_tag: zero-shot-classification
tags:
  - onnx
  - deberta-v2
  - text-classification
  - zero-shot-classification
  - nli
  - schift
  - transformers.js
datasets:
  - nyu-mll/multi_nli
  - stanfordnlp/snli
---

# schift-nli

ONNX-quantized DeBERTa-v3-xsmall for Natural Language Inference, optimized for [Dot](https://schift.io) local inference.

- **Base model**: [cross-encoder/nli-deberta-v3-xsmall](https://huggingface.co/cross-encoder/nli-deberta-v3-xsmall)
- **ONNX source**: [Xenova/nli-deberta-v3-xsmall](https://huggingface.co/Xenova/nli-deberta-v3-xsmall)
- **Parameters**: 70.8M
- **Labels**: entailment, contradiction, neutral
- **Use case**: Intent routing, polarity detection, document pair classification

## Usage in Dot

Loaded automatically by Dot's local NLI classifier. No manual setup needed.

## Usage with Transformers.js

```js
import { pipeline } from '@huggingface/transformers';

const classifier = await pipeline('text-classification', 'schift-io/schift-nli', {
  quantized: true,
});

const result = await classifier(
  { text: 'A man is eating pizza', text_pair: 'A man is eating food' },
  { top_k: 3 }
);
// [{ label: 'entailment', score: 0.97 }, ...]
```

## License

Apache 2.0 (inherited from base model)