Upload ONNX optimized DeBERTa model with quantization

README.md

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+---
+language: multilingual
+license: mit
+tags:
+- zero-shot-classification
+- nli
+- onnx
+- optimized
+- deberta-v3
+base_model: MoritzLaurer/deberta-v3-large-zeroshot-v2.0
+---
+
+# DeBERTa-v3-large Zero-Shot Classification - ONNX
+
+This is an ONNX-optimized version of [`MoritzLaurer/deberta-v3-large-zeroshot-v2.0`](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0) for efficient inference.
+
+## Model Description
+
+This repository contains:
+- **model.onnx**: Regular ONNX exported model
+- **model_quantized.onnx**: INT8 dynamically quantized model for faster inference with minimal accuracy loss
+
+The model is optimized for zero-shot classification tasks across multiple languages.
+
+## Usage
+
+### Zero-Shot Classification Pipeline (Recommended)
+
+```python
+from transformers import pipeline, AutoTokenizer
+from optimum.onnxruntime import ORTModelForSequenceClassification
+
+# Load the quantized model
+model = ORTModelForSequenceClassification.from_pretrained(
+    "richardr1126/deberta-v3-large-zeroshot-v2.0-ONNX",
+    file_name="model_quantized.onnx"
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "richardr1126/deberta-v3-large-zeroshot-v2.0-ONNX"
+)
+
+# Patch the model's forward method to handle token_type_ids
+original_forward = model.forward
+def patched_forward(input_ids=None, attention_mask=None, token_type_ids=None, **kwargs):
+    return original_forward(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
+model.forward = patched_forward
+
+# Create zero-shot classification pipeline
+classifier = pipeline(
+    "zero-shot-classification",
+    model=model,
+    tokenizer=tokenizer,
+    device=-1  # CPU inference
+)
+
+# Define your labels
+labels = ["politics", "technology", "sports", "entertainment", "business"]
+
+# Classify text
+text = "Apple announced their new AI chip with impressive performance gains."
+result = classifier(
+    text,
+    candidate_labels=labels,
+    hypothesis_template="This text is about {}",
+    multi_label=True  # Enable multi-label classification
+)
+
+print(f"Text: {text}")
+for label, score in zip(result['labels'], result['scores']):
+    print(f"  {label}: {score:.2%}")
+```
+
+### Using Regular ONNX Model
+
+For the non-quantized model (larger but potentially slightly more accurate):
+
+```python
+model = ORTModelForSequenceClassification.from_pretrained(
+    "richardr1126/deberta-v3-large-zeroshot-v2.0-ONNX",
+    file_name="model.onnx"
+)
+# ... rest of the code is the same
+```
+
+## Performance
+
+The quantized model provides:
+- **Faster inference**: ~2-3x speedup compared to PyTorch
+- **Smaller size**: Reduced model size due to INT8 quantization
+- **Maintained accuracy**: Minimal accuracy loss (<1%) compared to the original model
+
+## Original Model
+
+This is an optimized version of the original model:
+- **Base Model**: [MoritzLaurer/deberta-v3-large-zeroshot-v2.0](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0)
+- **Architecture**: DeBERTa-v3-large
+- **Task**: Zero-shot classification / NLI
+
+## Optimization Details
+
+- **Export**: Converted from PyTorch to ONNX format
+- **Quantization**: Dynamic quantization with INT8 weights
+- **Framework**: ONNX Runtime with Optimum
+
+## License
+
+Same as the base model - MIT License
+
+## Citation
+
+If you use this model, please cite the original model:
+
+```bibtex
+@misc{laurer2022deberta,
+  author = {Laurer, Moritz and Atteveldt, Wouter van and Casas, Andreu Salleras and Welbers, Kasper},
+  title = {DeBERTa-v3-large Zero-Shot Classification},
+  year = {2022},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0}
+}
+```
+
+## Acknowledgments
+
+This ONNX optimization was created for efficient deployment in production environments. Special thanks to the original model authors and the Hugging Face Optimum team.

added_tokens.json

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+{
+  "[MASK]": 128000
+}

config.json

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+{
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "dtype": "float16",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "entailment",
+    "1": "not_entailment"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "entailment": 0,
+    "not_entailment": 1
+  },
+  "layer_norm_eps": 1e-07,
+  "legacy": true,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 1024,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "transformers_version": "4.57.0",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.onnx

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model_quantized.onnx

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special_tokens_map.json

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+{
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

spm.model

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+size 2464616

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+  "bos_token": "[CLS]",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "eos_token": "[SEP]",
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}