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  1. LICENSE +189 -0
  2. README.md +97 -0
  3. config.json +23 -0
  4. manifest.json +17 -0
  5. model_quantized.onnx +3 -0
  6. special_tokens_map.json +51 -0
  7. tokenizer.json +0 -0
  8. tokenizer_config.json +73 -0
  9. vocab.txt +0 -0
LICENSE ADDED
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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - onnx
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+ - int8
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+ - quantized
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+ - sentence-similarity
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+ - embeddings
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+ - justembed
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+ base_model: sentence-transformers/all-mpnet-base-v2
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+ library_name: onnxruntime
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+ pipeline_tag: feature-extraction
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+ ---
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+
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+ # MPNet INT8 — ONNX Quantized
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+
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+ ONNX INT8 quantized version of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for efficient general-purpose sentence embeddings.
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+
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+ ## Model Details
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+
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+ | Property | Value |
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+ |----------|-------|
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+ | Base Model | [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) |
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+ | Format | ONNX |
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+ | Quantization | INT8 (dynamic quantization) |
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+ | Embedding Dimension | 768 |
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+ | Quantized by | [JustEmbed](https://pypi.org/project/justembed/) |
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+
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+ ## What is this?
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+
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+ This is a quantized ONNX export of all-mpnet-base-v2, one of the best general-purpose sentence embedding models from the sentence-transformers library. It maps sentences and paragraphs to a 768-dimensional dense vector space. The INT8 quantization reduces model size and improves inference speed while maintaining high accuracy.
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+
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+ ## Use Cases
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+
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+ - Semantic text search
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+ - Sentence similarity
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+ - Clustering and topic modeling
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+ - Paraphrase detection
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+ - General-purpose text embeddings
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+
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+ ## Files
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+
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+ - `model_quantized.onnx` — INT8 quantized ONNX model
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+ - `tokenizer.json` — Fast tokenizer
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+ - `vocab.txt` — Vocabulary file
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+ - `config.json` — Model configuration
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+
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+ ## Usage with JustEmbed
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+
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+ ```python
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+ from justembed import Embedder
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+
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+ embedder = Embedder("mpnet-int8")
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+ vectors = embedder.embed(["This is a sentence", "This is another sentence"])
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+ ```
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+
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+ ## Usage with ONNX Runtime
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+
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+ ```python
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+ import onnxruntime as ort
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+ from transformers import AutoTokenizer
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+
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+ tokenizer = AutoTokenizer.from_pretrained(".")
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+ session = ort.InferenceSession("model_quantized.onnx")
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+
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+ inputs = tokenizer("This is a sentence", return_tensors="np")
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+ outputs = session.run(None, dict(inputs))
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+ ```
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+
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+ ## Quantization Details
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+
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+ - Method: Dynamic INT8 quantization via ONNX Runtime
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+ - Source: Original PyTorch weights converted to ONNX, then quantized
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+ - Speed: ~2-3x faster inference than FP32
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+ - Size: ~4x smaller than FP32
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+
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+ ## License
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+
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+ This model is a derivative work of [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2).
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+
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+ The original model is licensed under **Apache License 2.0**. This quantized version is distributed under the same license. See the [LICENSE](LICENSE) file for the full text.
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @inproceedings{song2020mpnet,
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+ title={MPNet: Masked and Permuted Pre-training for Language Understanding},
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+ author={Song, Kaitao and Tan, Xu and Qin, Tao and Lu, Jianfeng and Liu, Tie-Yan},
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+ booktitle={NeurIPS},
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+ year={2020}
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+ }
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+ ```
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+
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+ ## Acknowledgments
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+
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+ - Original model by [UKP Lab / sentence-transformers](https://www.sbert.net/)
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+ - Quantization and packaging by [JustEmbed](https://pypi.org/project/justembed/)
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+ "vocab_size": 30527
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+ }
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