Update README.md

Author: ADITYADAS1999

examples/README.md

@@ -1,60 +1,5 @@
-# KerasNLP Example Models
+# KerasNLP Examples
 
-This examples directory contains complete recipes for training popular model
-architectures using KerasNLP. These are not part of the library itself, but
-rather serve to demonstrate how to use the library for common tasks, while
-simultaneously providing a mechanism to rigorously test library components.
-
-This directory is complementary to the NLP examples on
-[keras.io](https://keras.io/examples/). If you want to contribute a KerasNLP
-example and you're not sure if it should live on keras.io or in this directory,
-here's how they differ:
-
-- If an example can fit in < 300 lines of code and run in a Colab,
-  put it on keras.io.
-- If an example is too big for a single script or has high compute requirements,
-  add it here.
-
-In general, we will have a fairly high bar for what models we support in this
-directory. They should be widely used, practical models for solving standard
-problems in NLP.
-
-## Anatomy of an example
-
-Given a model named `modelname`, which involves both pretraining and finetuning
-on a downstream task, the contents of the `modelname` directory should be as
-follows:
-
-```shell
-modelname
-├── README.md
-├── __init__.py
-├── modelname_config.py
-├── modelname_model.py
-├── modelname_preprocess.py
-├── modelname_train.py
-└── modelname_finetune_X.py
-```
-
-- `README.md`: The README should contain complete instructions for downloading
-  data and training a model from scratch.
-- `__init__.py`: Empty (it's for imports).
-- `modelname_config.py`: This file should contain most of the configuration for
-  the model architecture, learning rate, etc, using simple Python constants. We
-  would like to avoid complex configuration setups (json, yaml, etc).
-- `modelname_preprocess.py`: If necessary. Standalone script to preprocess
-  inputs. If possible, prefer doing preprocessing dynamically with tf.data
-  inside the training and finetuning scripts.
-- `modelname_model.py`: This file should contain the actual `keras.Model` and
-  any custom layers needed for the example. Use KerasNLP components where ever
-  possible.
-- `modelname_train.py`: This file should be a runnable training script for
-  pretraining. If possible, this script should preprocess data dynamically
-  during training using `tf.data` and KerasNLP components (e.g. tokenizers).
-- `modelname_finetune_X.py`: Optional. There can be any number of these files,
-  for each task `X` we would like to support for finetuning. The file should be
-  a runnable training script which loads and finetunes a pretrained model.
-
-## Instructions for running on Google Cloud
-
-TODO(https://github.com/keras-team/keras-nlp/issues/178)
+The `examples/` directly contains scripts built on top of the library that do not fit well into
+the colab format used on [keras.io](https://keras.io/examples/). This includes recipes for
+pre-training models and evaluating models on benchmarks such as GLUE.