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--- |
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license: apache-2.0 |
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library_name: tfhub |
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language: en |
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tags: |
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- text |
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- sentence-similarity |
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- use |
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- universal-sentence-encoder |
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- dan |
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- tensorflow |
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--- |
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## Model name: universal-sentence-encoder |
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## Description adapted from [TFHub](https://tfhub.dev/google/universal-sentence-encoder/4) |
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# Overview |
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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. |
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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 variable length English text and the output is a 512 dimensional vector. We apply this model to the [STS benchmark](https://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) for semantic similarity, and the results can be seen in the [example notebook](https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb) made available. The universal-sentence-encoder model is trained with a deep averaging network (DAN) encoder. |
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To learn more about text embeddings, refer to the [TensorFlow Embeddings](https://www.tensorflow.org/tutorials/text/word_embeddings) documentation. Our encoder differs from word level embedding models in that we train on a number of natural language prediction tasks that require modeling the meaning of word sequences rather than just individual words. Details are available in the paper "Universal Sentence Encoder" [1]. |
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## Universal Sentence Encoder family |
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There are several versions of universal sentence encoder models trained with different goals including size/performance multilingual, and fine-grained question answer retrieval. |
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- [Universal Sentence Encoder family](https://tfhub.dev/google/collections/universal-sentence-encoder/1) |
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### Example use |
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### Using TF Hub and HF Hub |
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``` |
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model_path = snapshot_download(repo_id="Dimitre/universal-sentence-encoder") |
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model = KerasLayer(handle=model_path) |
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embeddings = model([ |
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"The quick brown fox jumps over the lazy dog.", |
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"I am a sentence for which I would like to get its embedding"]) |
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print(embeddings) |
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# The following are example embedding output of 512 dimensions per sentence |
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# Embedding for: The quick brown fox jumps over the lazy dog. |
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# [-0.03133016 -0.06338634 -0.01607501, ...] |
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# Embedding for: I am a sentence for which I would like to get its embedding. |
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# [0.05080863 -0.0165243 0.01573782, ...] |
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``` |
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### Using [TF Hub fork](https://github.com/dimitreOliveira/hub) |
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``` |
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model = pull_from_hub(repo_id="Dimitre/universal-sentence-encoder") |
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embeddings = model([ |
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"The quick brown fox jumps over the lazy dog.", |
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"I am a sentence for which I would like to get its embedding"]) |
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print(embeddings) |
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# The following are example embedding output of 512 dimensions per sentence |
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# Embedding for: The quick brown fox jumps over the lazy dog. |
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# [-0.03133016 -0.06338634 -0.01607501, ...] |
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# Embedding for: I am a sentence for which I would like to get its embedding. |
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# [0.05080863 -0.0165243 0.01573782, ...] |
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``` |
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This module is about 1GB. Depending on your network speed, it might take a while to load the first time you run inference with it. After that, loading the model should be faster as modules are cached by default ([learn more about caching](https://www.tensorflow.org/hub/tf2_saved_model)). Further, once a module is loaded to memory, inference time should be relatively fast. |
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### Preprocessing |
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The module does not require preprocessing the data before applying the module, it performs best effort text input preprocessing inside the graph. |
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# Semantic Similarity |
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Semantic similarity is a measure of the degree to which two pieces of text carry the same meaning. This is broadly useful in obtaining good coverage over the numerous ways that a thought can be expressed using language without needing to manually enumerate them. |
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Simple applications include improving the coverage of systems that trigger behaviors on certain keywords, phrases or utterances. [This section of the notebook](https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/semantic_similarity_with_tf_hub_universal_encoder.ipynb#scrollTo=BnvjATdy64eR) shows how to encode text and compare encoding distances as a proxy for semantic similarity. |
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# Classification |
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[This notebook](https://colab.research.google.com/github/tensorflow/hub/blob/master/docs/tutorials/text_classification_with_tf_hub.ipynb) shows how to train a simple binary text classifier on top of any TF-Hub module that can embed sentences. The Universal Sentence Encoder was partially trained with custom text classification tasks in mind. These kinds of classifiers can be trained to perform a wide variety of classification tasks often with a very small amount of labeled examples. |
