add scenarios table
@@ -20,14 +20,19 @@ In an era of transfer learning, transformers, and deep architectures, we believe
> [*GLUE Leaderboard*](https://gluebenchmark.com/leaderboard)
> [*SQuAD Leaderbord*](https://rajpurkar.github.io/SQuAD-explorer/)
+## Content
+
+The following is a summary of the scenarios covered in the repository. Each scenario is demonstrated in one or more Jupyter notebook examples that make use of the core code base of models and utilities.
+
+| Scenario | Applications | Models |
+|---| ------------------------ | ------------------- |
+|[Text Classification](scenarios/text_classification) |Topic Classification|BERT|
+|[Named Entity Recognition](scenarios/named_entity_recognition) |Wikipedia NER |BERT|
+|[Entailment](scenarios/entailment)|XNLI Natural Language Inference|BERT|
+|[Question Answering](scenarios/question_answering) |SQuAD | BiDAF|
+|[Sentence Similarity](scenarios/sentence_similarity) |STS Benchmark |Representation: TF-IDF, Word Embeddings, Doc Embeddings<br>Metrics: Cosine Similarity, Word Mover's Distance|
+|[Embeddings](scenarios/embeddings)| Custom Embeddings Training|Word2Vec<br>fastText<br>GloVe|
-The following is a list of typical scenarios that we aim at covering.
-- Text Classification
-- Named Entity Recognition
-- Text Similarity/Matching
-- Question Answering
-- Text Summarization
-- Machine Translation
## Getting Started
