Universal Sentence Encoder Multilingual Large (tfhub_use_multi_lg)

Description

The Universal Sentence Encoder encodes text into high-dimensional vectors that can be used for text classification, semantic similarity, clustering, and other natural language tasks.

The model is trained and optimized for greater-than-word length text, such as sentences, phrases, or short paragraphs. It is trained on a variety of data sources and a variety of tasks with the aim of dynamically accommodating a wide variety of natural language understanding tasks. The input is the variable-length text and the output is a 512-dimensional vector. The universal-sentence-encoder model has trained with a deep averaging network (DAN) encoder.

This model supports 16 languages (Arabic, Chinese-simplified, Chinese-traditional, English, French, German, Italian, Japanese, Korean, Dutch, Polish, Portuguese, Spanish, Thai, Turkish, Russian) text encoder.

The details are described in the paper “Multilingual Universal Sentence Encoder for Semantic Retrieval”.

Note: This model only works on Linux and macOS operating systems and is not compatible with Windows due to the incompatibility of the SentencePiece library.

Download

How to use

embeddings = UniversalSentenceEncoder.pretrained("tfhub_use_multi_lg", "xx") \
      .setInputCols("sentence") \
      .setOutputCol("sentence_embeddings")
val embeddings = UniversalSentenceEncoder.pretrained("tfhub_use_multi_lg", "xx")
      .setInputCols("sentence")
      .setOutputCol("sentence_embeddings")
import nlu

text = ["I love NLP", "Me encanta usar SparkNLP"]
embeddings_df = nlu.load('xx.use.multi_lg').predict(text, output_level='sentence')
embeddings_df

Results

It gives a 512-dimensional vector of the sentences.

Model Information

Model Name: tfhub_use_multi_lg
Compatibility: Spark NLP 3.0.0+
License: Open Source
Edition: Official
Input Labels: [sentence]
Output Labels: [sentence_embeddings]
Language: xx

Data Source

This embeddings model is imported from https://tfhub.dev/google/universal-sentence-encoder-multilingual-large/3

Benchmarking

- We apply this model to the STS benchmark for semantic similarity. Results are shown below:


STSBenchmark                       | dev    | test  |
-----------------------------------|--------|-------|   
Correlation coefficient of Pearson | 0.837  | 0.825 |


- For semantic similarity retrieval, we evaluate the model on [Quora and AskUbuntu retrieval task.](https://arxiv.org/abs/1811.08008). Results are shown below:


Dataset                | Quora | AskUbuntu | Average |
-----------------------|-------|-----------|---------|
Mean Average Precision  | 89.1  | 42.3      | 65.7    |


- For the translation pair retrieval, we evaluate the model on the United Nation Parallel Corpus. Results are shown below:

Language Pair  | en-es  | en-fr | en-ru | en-zh |
---------------|--------|-------|-------|-------|
Precision@1    | 86.1   | 83.3  | 88.9  | 78.8  |