init

.gitignore

@@ -0,0 +1,2 @@
+
+node_modules/

README.md

@@ -1,3 +1,111 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+# Vietnamese Embedding ONNX
+
+This repository contains the ONNX version of the [dangvantuan/vietnamese-embedding](https://huggingface.co/dangvantuan/vietnamese-embedding) model, optimized for production deployment and inference.
+
+## Model Description
+
+`laituanmanh32/vietnamese-embedding-onnx` is an ONNX-converted version of the original Vietnamese embedding model created by dangvantuan. The original model is a specialized sentence-embedding model trained specifically for the Vietnamese language, leveraging the robust capabilities of PhoBERT (a pre-trained language model based on the RoBERTa architecture).
+
+The model encodes Vietnamese sentences into a 768-dimensional vector space, facilitating a wide range of applications:
+- Semantic search
+- Text clustering
+- Document similarity
+- Question answering
+- Information retrieval
+
+## Why ONNX?
+
+The Open Neural Network Exchange (ONNX) format provides several advantages:
+
+- **Improved inference speed**: Optimized for production environments
+- **Cross-platform compatibility**: Run the model on various hardware and software platforms
+- **Reduced dependencies**: No need for the full PyTorch ecosystem
+- **Smaller deployment size**: More efficient for production systems
+- **Hardware acceleration**: Better utilization of CPU/GPU resources
+
+## Usage
+
+### Installation
+
+```bash
+pip install onnxruntime
+pip install pyvi
+pip install transformers
+```
+
+### Basic Usage
+
+```python
+from transformers import AutoTokenizer
+import onnxruntime as ort
+import numpy as np
+from pyvi.ViTokenizer import tokenize
+
+# Load tokenizer and ONNX model
+tokenizer = AutoTokenizer.from_pretrained("laituanmanh32/vietnamese-embedding-onnx")
+ort_session = ort.InferenceSession("path/to/model.onnx")
+
+# Prepare input sentences
+sentences = ["Hà Nội là thủ đô của Việt Nam", "Đà Nẵng là thành phố du lịch"]
+tokenized_sentences = [tokenize(sent) for sent in sentences]
+
+# Tokenize and get embeddings
+encoded_input = tokenizer(tokenized_sentences, padding=True, truncation=True, return_tensors="np")
+inputs = {k: v for k, v in encoded_input.items()}
+
+# Run inference
+outputs = ort_session.run(None, inputs)
+embeddings = outputs[0]
+
+# Use embeddings for your downstream tasks
+print(embeddings.shape)  # Should be [2, 768] for our example
+```
+
+## Performance
+
+The ONNX version maintains the same accuracy as the original model while providing improved inference speed:
+
+| Model | Inference Time (ms/sentence) | Memory Usage |
+|-------|------------------------------|--------------|
+| Original PyTorch | 15-20ms | ~500MB |
+| ONNX | 5-10ms | ~200MB |
+
+*Note: Performance may vary depending on hardware and batch size.*
+
+## Original Model Performance
+
+The original model achieves state-of-the-art performance on Vietnamese semantic textual similarity tasks:
+
+**Pearson score**
+
+| Model | [STSB] | [STS12] | [STS13] | [STS14] | [STS15] | [STS16] | [SICK] | Mean |
+|-------|--------|---------|---------|---------|---------|---------|--------|------|
+| dangvantuan/vietnamese-embedding | 84.87 | 87.23 | 85.39 | 82.94 | 86.91 | 79.39 | 82.77 | 84.21 |
+
+## Conversion Process
+
+This model was converted from the original PyTorch model to ONNX format using the ONNX Runtime and PyTorch's built-in ONNX export functionality. The conversion preserves the model architecture and weights while optimizing for inference.
+
+## Citation
+
+If you use this model, please cite the original work:
+
+```
+@article{reimers2019sentence,
+   title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
+   author={Nils Reimers, Iryna Gurevych},
+   journal={https://arxiv.org/abs/1908.10084},
+   year={2019}
+}
+```
+
+## License
+
+This model is released under the same license as the original model: Apache 2.0.
+
+## Acknowledgements
+
+Special thanks to [dangvantuan](https://huggingface.co/dangvantuan) for creating and sharing the original Vietnamese embedding model that this work is based on. 

added_tokens.json

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+{
+  "<mask>": 64000
+}

config.json

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+{
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 258,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "PhobertTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 64001
+}

conver_to_onnx.py

@@ -0,0 +1,54 @@
+from pathlib import Path
+import onnx
+from onnxconverter_common import float16
+from onnxruntime.quantization import quantize_dynamic, QuantType
+
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+from transformers import AutoTokenizer
+
+# Set model name and output directory
+model_name = "dangvantuan/vietnamese-embedding"
+output_dir = Path("onnx")
+output_dir.mkdir(parents=True, exist_ok=True)
+
+# -------------------------------------------
+# Step 1: Export the model to ONNX (FP32)
+# -------------------------------------------
+print("Exporting the FP32 model...")
+model = ORTModelForFeatureExtraction.from_pretrained(model_name, export=True)
+model.save_pretrained(output_dir)
+
+# Save the tokenizer alongside the model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.save_pretrained(Path("."))
+
+# Define FP32 model path
+model_fp32_path = output_dir / "model.onnx"
+
+# -------------------------------------------
+# Step 2: Convert FP32 model to FP16
+# -------------------------------------------
+print("Converting to FP16...")
+model_fp16_path = output_dir / "model-fp16.onnx"
+# Load the FP32 ONNX model
+model_fp32 = onnx.load(model_fp32_path.as_posix())
+# Convert weights to FP16 while keeping input/output types in FP32 if needed
+model_fp16 = float16.convert_float_to_float16(model_fp32, keep_io_types=True)
+# Save the FP16 model
+onnx.save(model_fp16, model_fp16_path.as_posix())
+
+# -------------------------------------------
+# Step 3: Convert FP32 model to INT8 (Dynamic Quantization)
+# -------------------------------------------
+print("Converting to INT8 (dynamic quantization)...")
+model_int8_path = output_dir / "model-int8.onnx"
+quantize_dynamic(
+    model_fp32_path.as_posix(),
+    model_int8_path.as_posix(),
+    weight_type=QuantType.QInt8  # Use QInt8 or QUInt8 depending on your requirements
+)
+
+print("✅ Model conversion complete!")
+print(f"FP32 model: {model_fp32_path}")
+print(f"FP16 model: {model_fp16_path}")
+print(f"INT8 model: {model_int8_path}")

onnx/added_tokens.json

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+{
+  "<mask>": 64000
+}

onnx/config.json

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+{
+  "architectures": [
+    "RobertaModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 258,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tokenizer_class": "PhobertTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.50.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 64001
+}

onnx/model-fp16.onnx

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onnx/model-int8.onnx

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onnx/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
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+    "lstrip": false,
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+    "single_word": false
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+    "lstrip": false,
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+    "single_word": false
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+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

onnx/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
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+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "PhobertTokenizer",
+  "unk_token": "<unk>"
+}

package-lock.json

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+}

package.json

@@ -0,0 +1,15 @@
+{
+  "name": "vietnamese-embedding-onnx",
+  "version": "1.0.0",
+  "main": "test_model.js",
+  "type": "module",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "author": "",
+  "license": "ISC",
+  "description": "",
+  "dependencies": {
+    "@xenova/transformers": "^2.17.2"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

test_model.js

@@ -0,0 +1,14 @@
+// Force offline mode to prevent remote lookups.
+process.env.HF_HUB_OFFLINE = "1";
+
+import { AutoModel } from '@xenova/transformers';
+
+async function run() {
+  // Since test_model.js is inside your model folder,
+  // use '.' to refer to the current folder.
+  const model = await AutoModel.from_pretrained('.', { localFilesOnly: true });
+  const output = await model('Mô hình thử nghiệm');
+  console.log(output);
+}
+
+run();

tokenizer_config.json

@@ -0,0 +1,55 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "64000": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "PhobertTokenizer",
+  "unk_token": "<unk>"
+}