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README.md

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+---
+license: apache-2.0
+tags:
+  - onnx
+  - int8
+  - quantized
+  - scientific
+  - embeddings
+  - justembed
+base_model: allenai/scibert_scivocab_uncased
+library_name: onnxruntime
+pipeline_tag: feature-extraction
+---
+
+# SciBERT INT8 — ONNX Quantized
+
+ONNX INT8 quantized version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) for efficient scientific text embeddings.
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Base Model | [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) |
+| Format | ONNX |
+| Quantization | INT8 (dynamic quantization) |
+| Embedding Dimension | 768 |
+| Quantized by | [JustEmbed](https://pypi.org/project/justembed/) |
+
+## What is this?
+
+This is a quantized ONNX export of SciBERT, a BERT model trained on a large corpus of scientific text (1.14M papers, 3.1B tokens from Semantic Scholar) by the Allen Institute for AI. The INT8 quantization reduces model size and improves inference speed while maintaining high accuracy for scientific domain embeddings.
+
+## Use Cases
+
+- Scientific paper search and retrieval
+- Research document similarity
+- Academic text classification
+- Scientific entity recognition embeddings
+- Citation recommendation
+
+## Files
+
+- `model_quantized.onnx` — INT8 quantized ONNX model
+- `tokenizer.json` — Fast tokenizer
+- `vocab.txt` — Scientific vocabulary
+- `config.json` — Model configuration
+
+## Usage with JustEmbed
+
+```python
+from justembed import Embedder
+
+embedder = Embedder("scibert-int8")
+vectors = embedder.embed(["neural network architectures for NLP"])
+```
+
+## Usage with ONNX Runtime
+
+```python
+import onnxruntime as ort
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(".")
+session = ort.InferenceSession("model_quantized.onnx")
+
+inputs = tokenizer("neural network architectures for NLP", return_tensors="np")
+outputs = session.run(None, dict(inputs))
+```
+
+## Quantization Details
+
+- Method: Dynamic INT8 quantization via ONNX Runtime
+- Source: Original PyTorch weights converted to ONNX, then quantized
+- Speed: ~2-3x faster inference than FP32
+- Size: ~4x smaller than FP32
+
+## License
+
+This model is a derivative work of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased).
+
+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.
+
+## Citation
+
+```bibtex
+@inproceedings{beltagy2019scibert,
+  title={SciBERT: A Pretrained Language Model for Scientific Text},
+  author={Beltagy, Iz and Lo, Kyle and Cohan, Arman},
+  booktitle={Proceedings of EMNLP},
+  year={2019}
+}
+```
+
+## Acknowledgments
+
+- Original model by [Allen Institute for AI](https://allenai.org/)
+- Quantization and packaging by [JustEmbed](https://pypi.org/project/justembed/)

config.json

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+{
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "type_vocab_size": 2,
+  "vocab_size": 31090
+}

manifest.json

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+{
+  "schema_version": "1.0",
+  "name": "scibert-int8",
+  "version": "1.0.1",
+  "type": "single",
+  "model_class": "BERTEmbedder",
+  "embedding_dim": 768,
+  "min_justembed_version": "0.1.1a9",
+  "description": "Scientific paper embeddings for academic research and technical literature (INT8 quantized)",
+  "files": null,
+  "metadata": null,
+  "stages": null,
+  "max_sequence_length": 512,
+  "license": "Apache-2.0",
+  "source": "allenai/scibert_scivocab_uncased",
+  "quantization": "int8"
+}

model_quantized.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c4ddf36fd48b30c40493ee6b95143ebb663a77a0912445c6488b023d8c9e5f09
+size 111527119

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}