Adding README

README.md

@@ -1,3 +1,80 @@
 ---
 license: apache-2.0
+library_name: tfhub
+language: en
+tags:
+- text
+- sentence-similarity
+- use
+- universal-sentence-encoder
+- tensorflow
 ---
+
+# Overview
+
+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 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.
+
+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].
+
+## Universal Sentence Encoder family
+
+There are several versions of universal sentence encoder models trained with different goals including size/performance multilingual, and fine-grained question answer retrieval.
+
+- [Universal Sentence Encoder family](https://tfhub.dev/google/collections/universal-sentence-encoder/1)
+
+### Example use
+
+
+### Using TF Hub and HF Hub
+```
+model_path = snapshot_download(repo_id="Dimitre/universal-sentence-encoder")
+model =  KerasLayer(handle=model_path)
+embeddings = model([
+    "The quick brown fox jumps over the lazy dog.",
+    "I am a sentence for which I would like to get its embedding"])
+    
+print(embeddings)
+
+# The following are example embedding output of 512 dimensions per sentence
+# Embedding for: The quick brown fox jumps over the lazy dog.
+# [-0.03133016 -0.06338634 -0.01607501, ...]
+# Embedding for: I am a sentence for which I would like to get its embedding.
+# [0.05080863 -0.0165243   0.01573782, ...]
+```
+
+### Using [TF Hub fork](https://github.com/dimitreOliveira/hub)
+```
+model = pull_from_hub(repo_id="Dimitre/universal-sentence-encoder")
+embeddings = model([
+    "The quick brown fox jumps over the lazy dog.",
+    "I am a sentence for which I would like to get its embedding"])
+
+print(embeddings)
+
+# The following are example embedding output of 512 dimensions per sentence
+# Embedding for: The quick brown fox jumps over the lazy dog.
+# [-0.03133016 -0.06338634 -0.01607501, ...]
+# Embedding for: I am a sentence for which I would like to get its embedding.
+# [0.05080863 -0.0165243   0.01573782, ...]
+```
+
+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.
+
+### Preprocessing
+
+The module does not require preprocessing the data before applying the module, it performs best effort text input preprocessing inside the graph.
+
+# Semantic Similarity
+
+![Semantic Similarity Graphic](https://www.gstatic.com/aihub/tfhub/universal-sentence-encoder/example-similarity.png)
+
+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.
+
+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.
+
+# Classification
+![Text Classification Graphic](https://www.gstatic.com/aihub/tfhub/universal-sentence-encoder/example-classification.png)
+
+[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.