add content to README.md
+# Word Embedding
+
+This folder contains examples and best practices, written in Jupyter notebooks, for training vector representations with word embedding.
+
+# What is Word Embedding?
+
+Word embedding is a technique to map words or phrases from the vocabulary to vectors or real numbers. The learned vector representations of words capture syntactic and semantic word relationships and therefore can be very useful for tasks like sentence similary, text classifcation. There are three typical ways for word embedding: [Word2Vec](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf), [GloVe](https://nlp.stanford.edu/pubs/glove.pdf), and [FastText](https://arxiv.org/abs/1607.01759). All of the three methods provide pretrained models ([pretrained model with Word2Vec](https://code.google.com/archive/p/word2vec/), [pretrained model with Glove](https://github.com/stanfordnlp/GloVe), [pretrained model with Fasttext](https://fasttext.cc/docs/en/crawl-vectors.html)). These pretrained models in general are trained with general corpus like Wikipedia data,Common Crawl data, etc and may not server well for situations where you have a domain-specific language learning problem or there is no pretrained model for the language you need to work with. In this folder, we provide examples how apply each of the three method to train your own word embeddings.
+
+
+## Summary
+
+The following summarizes each notebook for Embedding. Each notebook provides more details and guiding in principles on building state of the art models.
+
+|Notebook|Runs Local|Description|
+|---|---|---|
+|[Developing Word Embeddings](embedding_trainer.ipynb)| Yes| A notebook shows how to learn word representation with Word2Vec, Fasttext and Glove from [STS Benchmark dataset](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark#STS_benchmark_dataset_and_companion_dataset) |
