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README.md

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+---
+license: mit
+language:
+  - en
+base_model: MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c
+pipeline_tag: text-classification
+library_name: transformers
+tags:
+  - onnx
+  - optimum
+  - deberta-v3
+  - nli
+  - natural-language-inference
+  - zero-shot-classification
+  - cross-encoder
+  - text-classification
+datasets:
+  - nyu-mll/multi_nli
+  - fever
+---
+
+# DeBERTa-v3-base-mnli-fever-docnli-ling-2c-onnx
+
+ONNX exports of [`MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c`](https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c), a DeBERTa-v3-base NLI cross-encoder, packaged for CPU inference with ONNX Runtime.
+
+This repository only re-packages the base model as ONNX — **no additional training was performed.**
+
+## Variants
+
+| Subfolder | File | Precision | Size | Notes |
+|---|---|---|---|---|
+| `nli-onnx/` | `model.onnx` | fp32, O3-optimized | ~739 MB | Highest accuracy |
+| `nli-onnx-quantized/` | `model_quantized.onnx` | INT8 (AVX-512 VNNI) | ~233 MB | ~3× smaller, CPU-optimized |
+
+Each subfolder is self-contained (ONNX graph + tokenizer + config).
+
+## Labels
+
+Binary NLI (2-class). Apply softmax to the logits; the entailment probability is `softmax(logits)[0]`.
+
+| id | label |
+|---|---|
+| 0 | entailment |
+| 1 | not_entailment |
+
+## Usage
+
+```python
+import onnxruntime as ort, numpy as np
+from transformers import AutoTokenizer
+
+repo = "Hanno-Labs/DeBERTa-v3-base-mnli-fever-docnli-ling-2c-onnx"
+sub = "nli-onnx-quantized"  # or "nli-onnx" for fp32
+tok = AutoTokenizer.from_pretrained(repo, subfolder=sub)
+sess = ort.InferenceSession(f"{sub}/model_quantized.onnx", providers=["CPUExecutionProvider"])
+
+enc = tok("A cat sat on the mat.", "There is a cat.", return_tensors="np", truncation=True)
+logits = sess.run(None, {k: v for k, v in enc.items()})[0]
+probs = np.exp(logits) / np.exp(logits).sum(-1, keepdims=True)
+print("entailment prob:", float(probs[0, 0]))
+```
+
+(Also loadable via `optimum.onnxruntime.ORTModelForSequenceClassification.from_pretrained(repo, subfolder=sub)`.)
+
+## How these were exported
+
+```bash
+# fp32 (O3):
+optimum-cli export onnx \
+  --model MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c \
+  --task text-classification --optimize O3 nli-onnx/
+
+# INT8 quantized (export without O3 first — O3 breaks quantization):
+optimum-cli export onnx \
+  --model MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c \
+  --task text-classification nli-onnx-temp/
+optimum-cli onnxruntime quantize \
+  --onnx_model nli-onnx-temp --avx512_vnni -o nli-onnx-quantized/
+```
+
+## License & attribution
+
+**MIT**, inherited from the base model. Base architecture: DeBERTa-v3-base (Microsoft). Packaged by Hanno-Labs.
+
+If you use this model, please cite the original author:
+
+> Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. *Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI.*