pipeline_tag, language, tags, license, datasets

pipeline_tag	language	tags	license	datasets
sentence-similarity	af am ar as az be bg bn bo bs ca ceb co cs cy da de el en eo es et eu fa fi fr fy ga gd gl gu ha haw he hi hmn hr ht hu hy id ig is it ja jv ka kk km kn ko ku ky la lb lo lt lv mg mi mk ml mn mr ms mt my ne nl no ny or pa pl pt ro ru rw si sk sl sm sn so sq sr st su sv sw ta te tg th tk tl tr tt ug uk ur uz vi wo xh yi yo zh zu	bert sentence_embedding multilingual google sentence-similarity	apache-2.0	CommonCrawl Wikipedia

LaBSE

Model description

Language-agnostic BERT Sentence Encoder (LaBSE) is a BERT-based model trained for sentence embedding for 109 languages. The pre-training process combines masked language modeling with translation language modeling. The model is useful for getting multilingual sentence embeddings and for bi-text retrieval.

• Model: HuggingFace's model hub.
• Paper: arXiv.
• Original model: TensorFlow Hub.
• Blog post: Google AI Blog.
• Conversion from TensorFlow to PyTorch: GitHub.

This is migrated from the v2 model on the TF Hub, which uses dict-based input. The embeddings produced by both the versions of the model are equivalent.

Usage

Using the model:

import torch
from transformers import BertModel, BertTokenizerFast


tokenizer = BertTokenizerFast.from_pretrained("setu4993/LaBSE")
model = BertModel.from_pretrained("setu4993/LaBSE")
model = model.eval()

english_sentences = [
    "dog",
    "Puppies are nice.",
    "I enjoy taking long walks along the beach with my dog.",
]
english_inputs = tokenizer(english_sentences, return_tensors="pt", padding=True)

with torch.no_grad():
    english_outputs = model(**english_inputs)

To get the sentence embeddings, use the pooler output:

english_embeddings = english_outputs.pooler_output

Output for other languages:

italian_sentences = [
    "cane",
    "I cuccioli sono carini.",
    "Mi piace fare lunghe passeggiate lungo la spiaggia con il mio cane.",
]
japanese_sentences = ["犬", "子犬はいいです", "私は犬と一緒にビーチを散歩するのが好きです"]
italian_inputs = tokenizer(italian_sentences, return_tensors="pt", padding=True)
japanese_inputs = tokenizer(japanese_sentences, return_tensors="pt", padding=True)

with torch.no_grad():
    italian_outputs = model(**italian_inputs)
    japanese_outputs = model(**japanese_inputs)

italian_embeddings = italian_outputs.pooler_output
japanese_embeddings = japanese_outputs.pooler_output

For similarity between sentences, an L2-norm is recommended before calculating the similarity:

import torch.nn.functional as F


def similarity(embeddings_1, embeddings_2):
    normalized_embeddings_1 = F.normalize(embeddings_1, p=2)
    normalized_embeddings_2 = F.normalize(embeddings_2, p=2)
    return torch.matmul(
        normalized_embeddings_1, normalized_embeddings_2.transpose(0, 1)
    )


print(similarity(english_embeddings, italian_embeddings))
print(similarity(english_embeddings, japanese_embeddings))
print(similarity(italian_embeddings, japanese_embeddings))

Details

Details about data, training, evaluation and performance metrics are available in the original paper.

BibTeX entry and citation info

@misc{feng2020languageagnostic,
      title={Language-agnostic BERT Sentence Embedding},
      author={Fangxiaoyu Feng and Yinfei Yang and Daniel Cer and Naveen Arivazhagan and Wei Wang},
      year={2020},
      eprint={2007.01852},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}