Update readme for entailment scenario
# Natural Language Inference (NLI)
-Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is the
-task of classifying a pair of premise and hypothesis sentences into three
-classes: contradiction, neutral, and entailment. For example,
+
+This folder provides end-to-end examples of building Natural Language Inference (NLI) models. We
+demonstrate the typical workflow of data preprocessing and model building for NLI task and use the
+utility functions in the *utils_nlp* folder to speed up these processes.
+
+Natural Language Inference or Recognizing Textual Entailment (RTE) is the task of classifying
+a pair of premise and hypothesis sentences into three classes: contradiction, neutral, and
+entailment. For example,
|Premise|Hypothesis|Label|
|-------|----------|-----|
@@ -9,4 +14,16 @@ classes: contradiction, neutral, and entailment. For example,
|An older and younger man smiling.|Two men are smiling and laughing at the cats playing on the floor.|neutral|
|A soccer game with multiple males playing.|Some men are playing a sport.|entailment|
-NLI is one of many NLP tasks that require robust compositional sentence understanding, but it's simpler compared to other tasks like question answering and machine translation.
+NLI is one of many NLP tasks that require robust compositional sentence understanding, but it's
+simpler compared to other tasks like question answering and machine translation.
+
+Currently, we focus on fine-tuning pre-trained BERT models for NLI and use the utility classes and
+functions under [utils_nlp/models/bert](../../utils_nlp/models/bert/). We plan to continue adding
+new state-of-the-art models.
+
+The following notebooks are included in this folder
+
+|Notebook|Description|Dataset|
+|--------|-----------|-------|
+|entailment_multinli_bert.ipynb|Fine-tuning of pre-trained BERT model for NLI|[MultiNLI](https://www.nyu.edu/projects/bowman/multinli/)|
+|entailment_xnli_bert_azureml.ipynb|**Distributed** fine-tuning of pre-trained BERT model for NLI|[XNLI](https://www.nyu.edu/projects/bowman/xnli/)
