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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