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README.md

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+---
+base_model:
+- NghiemAbe/Vi-Legal-Bi-Encoder-v2
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- bnb-my-repo
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+language:
+- vi
+---
+# NghiemAbe/Vi-Legal-Bi-Encoder-v2 (Quantized)
+
+## Description
+This model is a quantized version of the original model [`NghiemAbe/Vi-Legal-Bi-Encoder-v2`](https://huggingface.co/NghiemAbe/Vi-Legal-Bi-Encoder-v2). 
+
+It's quantized using the BitsAndBytes library to 4-bit using the [bnb-my-repo](https://huggingface.co/spaces/bnb-community/bnb-my-repo) space.
+
+## Quantization Details
+- **Quantization Type**: int4
+- **bnb_4bit_quant_type**: nf4
+- **bnb_4bit_use_double_quant**: False
+- **bnb_4bit_compute_dtype**: float32
+- **bnb_4bit_quant_storage**: float32
+
+
+
+# 📄 Original Model Information
+
+
+
+# NghiemAbe/Vi-Legal-Bi-Encoder-v2
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+from pyvi.ViTokenizer import tokenize
+sentences = [tokenize("This is an example sentence"), tokenize("Each sentence is converted")]
+
+model = SentenceTransformer('NghiemAbe/Vi-Legal-Bi-Encoder-v2')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+# Sentences we want sentence embeddings for
+sentences = [tokenize("This is an example sentence"), tokenize("Each sentence is converted")]
+
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('NghiemAbe/Vi-Legal-Bi-Encoder-v2')
+model = AutoModel.from_pretrained('NghiemAbe/Vi-Legal-Bi-Encoder-v2')
+
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+
+
+
+## Evaluation Results
+
+I evaluated my [Dev-Legal-Dataset](https://huggingface.co/datasets/NghiemAbe/dev_legal) and here are the results:
+
+| Model                                                                  | R@1  | R@5  | R@10 | R@20 | R@100 | MRR@5 | MRR@10 | MRR@20 | MRR@100 | Avg  |
+|------------------------------------------------------------------------|------|------|------|------|-------|-------|--------|--------|---------|------|
+| keepitreal/vietnamese-sbert                                            | 0.278| 0.552| 0.649| 0.734| 0.842 | 0.396 | 0.409  | 0.415  | 0.417   | 0.521|
+| sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2            | 0.314| 0.486| 0.585| 0.662| 0.854 | 0.395 | 0.409  | 0.414  | 0.419   | 0.504|
+| sentence-transformers/paraphrase-multilingual-mpnet-base-v2            | 0.354| 0.553| 0.646| 0.750| 0.896 | 0.449 | 0.461  | 0.468  | 0.472   | 0.561|
+| intfloat/multilingual-e5-small                                         | 0.488| 0.746| 0.835| 0.906| 0.962 | 0.610 | 0.620  | 0.624  | 0.625   | 0.713|
+| intfloat/multilingual-e5-base                                          | 0.466| 0.740| 0.840| 0.907| 0.952 | 0.596 | 0.608  | 0.612  | 0.613   | 0.704|
+| bkai-foundation-models/vietnamese-bi-encoder                           | 0.644| 0.881| 0.924| 0.954| 0.986 | 0.752 | 0.757  | 0.758  | 0.759   | 0.824|
+| Vi-Legal-Bi-Encoder-v2                                                 | 0.720| 0.884| 0.935| 0.963| 0.986 | 0.796 | 0.802  | 0.803  | 0.804   | 0.855|

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,
+  "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",
+  "quantization_config": {
+    "_load_in_4bit": true,
+    "_load_in_8bit": false,
+    "bnb_4bit_compute_dtype": "float32",
+    "bnb_4bit_quant_storage": "float32",
+    "bnb_4bit_quant_type": "nf4",
+    "bnb_4bit_use_double_quant": false,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": null,
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": true,
+    "load_in_8bit": false,
+    "quant_method": "bitsandbytes"
+  },
+  "tokenizer_class": "PhobertTokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.53.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 64001
+}

model.safetensors

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+oid sha256:391f2ab12f6b0e3c7932dd3f10ece9aa2ec4ca593e4d526694faa2b7fbf61d1d
+size 248090248

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "lstrip": false,
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+  "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": {
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+    "rstrip": false,
+    "single_word": false
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+  "sep_token": {
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+    "single_word": false
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+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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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+      "single_word": false,
+      "special": true
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+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "PhobertTokenizer",
+  "unk_token": "<unk>"
+}