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NghiemAbe/Vi-Legal-Bi-Encoder-v2

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

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, 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.

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 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
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