diff --git a/README.md b/README.md
index b1590b77bb..1aa4f2ea0d 100644
--- a/README.md
+++ b/README.md
@@ -92,19 +92,17 @@ in AI research embedded into each task so you don't have to be a deep learning P
 ### Predictions
 
 ```python
+
 # import our libraries
-from flash.text import TextClassifier
+from flash.text import TranslationTask
 
 # 1. Load finetuned task
-model = TextClassifier.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/text_classification_model.pt")
+model = TranslationTask.load_from_checkpoint("https://flash-weights.s3.amazonaws.com/translation_model_en_ro.pt")
 
-# 2. Perform inference from list of sequences
+# 2. Translate a few sentences!
 predictions = model.predict([
-    "Turgid dialogue, feeble characterization - Harvey Keitel a judge?.",
-    "The worst movie in the history of cinema.",
-    "I come from Bulgaria where it 's almost impossible to have a tornado."
-    "Very, very afraid"
-    "This guy has done a great job with this movie!",
+    "BBC News went to meet one of the project's first graduates.",
+    "A recession has come as quickly as 11 months after the first rate hike and as long as 86 months.",
 ])
 print(predictions)
 ```
@@ -114,6 +112,7 @@ print(predictions)
 First, finetune:
 
 ```python
+# import our libraries
 import flash
 from flash import download_data
 from flash.vision import ImageClassificationData, ImageClassifier
@@ -157,109 +156,72 @@ print(predictions)
 ## Tasks
 Flash is built as a collection of community-built tasks. A task is highly opinionated and laser-focused on solving a single problem well, using state-of-the-art methods.
 
-### Example 1: Image classification
-Flash has an ImageClassification task to tackle any image classification problem.
+### Example 1: Image embedding
+Flash has an Image embedding task to encodes an image into a vector of image features which can be used for anything like clustering, similarity search or classification.
 
 <details>
   <summary>View example</summary>
-  To illustrate, Let's say we wanted to develop a model that could classify between ants and bees.
-
-  <img src="https://pl-flash-data.s3.amazonaws.com/images/ant_bee.png" width="300px">
-
-  Here we classify ants vs bees.
 
   ```python
-  import flash
-  from flash import download_data
-  from flash.vision import ImageClassificationData, ImageClassifier
+  # import our libraries
+  import torch
+
+  from flash.core.data import download_data
+  from flash.vision import ImageEmbedder
 
   # 1. Download the data
   download_data("https://pl-flash-data.s3.amazonaws.com/hymenoptera_data.zip", 'data/')
 
-  # 2. Load the data
-  datamodule = ImageClassificationData.from_folders(
-      train_folder="data/hymenoptera_data/train/",
-      valid_folder="data/hymenoptera_data/val/",
-      test_folder="data/hymenoptera_data/test/",
-  )
-
-  # 3. Build the model
-  model = ImageClassifier(num_classes=datamodule.num_classes)
-
-  # 4. Create the trainer. Run once on data
-  trainer = flash.Trainer(max_epochs=1)
-
-  # 5. Train the model
-  trainer.finetune(model, datamodule=datamodule, strategy="freeze_unfreeze")
+  # 2. Create an ImageEmbedder with resnet50 trained on imagenet.
+  embedder = ImageEmbedder(backbone="resnet50", embedding_dim=128)
 
-  # 6. Test the model
-  trainer.test()
-
-  # 7. Predict!
-  predictions = model.predict([
-      "data/hymenoptera_data/val/bees/65038344_52a45d090d.jpg",
-      "data/hymenoptera_data/val/bees/590318879_68cf112861.jpg",
-      "data/hymenoptera_data/val/ants/540543309_ddbb193ee5.jpg",
-  ])
-  print(predictions)
-  ```
+  # 3. Generate an embedding from an image path.
+  embeddings = embedder.predict('data/hymenoptera_data/predict/153783656_85f9c3ac70.jpg')
 
-  To run the example:
-  ```bash
-  python flash_examples/finetuning/image_classifier.py
+  # 4. Print embeddings shape
+  print(embeddings.shape)
   ```
 </details>
 
-### Example 2: Text Classification
-Flash has a TextClassification task to tackle any text classification problem.
+### Example 2: Text Summarization
+Flash has a Summarization task to sum up text from a larger article into a short description.
 
 <details>
   <summary>View example</summary>
-  To illustrate, say you wanted to classify movie reviews as positive or negative.
 
   ```python
+  # import our libraries
   import flash
   from flash import download_data
-  from flash.text import TextClassificationData, TextClassifier
+  from flash.text import SummarizationData, SummarizationTask
 
   # 1. Download the data
-  download_data("https://pl-flash-data.s3.amazonaws.com/imdb.zip", 'data/')
+  download_data("https://pl-flash-data.s3.amazonaws.com/xsum.zip", 'data/')
 
   # 2. Load the data
-  datamodule = TextClassificationData.from_files(
-      train_file="data/imdb/train.csv",
-      valid_file="data/imdb/valid.csv",
-      test_file="data/imdb/test.csv",
-      input="review",
-      target="sentiment",
-      batch_size=512
+  datamodule = SummarizationData.from_files(
+      train_file="data/xsum/train.csv",
+      valid_file="data/xsum/valid.csv",
+      test_file="data/xsum/test.csv",
+      input="input",
+      target="target"
   )
 
   # 3. Build the model
-  model = TextClassifier(num_classes=datamodule.num_classes)
+  model = SummarizationTask()
 
   # 4. Create the trainer. Run once on data
-  trainer = flash.Trainer(max_epochs=1)
+  trainer = flash.Trainer(max_epochs=1, gpus=1, precision=16)
 
   # 5. Fine-tune the model
-  trainer.finetune(model, datamodule=datamodule, strategy="freeze_unfreeze")
+  trainer.finetune(model, datamodule=datamodule)
 
   # 6. Test model
   trainer.test()
-
-  # 7. Classify a few sentences! How was the movie?
-  predictions = model.predict([
-      "Turgid dialogue, feeble characterization - Harvey Keitel a judge?.",
-      "The worst movie in the history of cinema.",
-      "I come from Bulgaria where it 's almost impossible to have a tornado."
-      "Very, very afraid"
-      "This guy has done a great job with this movie!",
-  ])
-  print(predictions)
   ```
   To run the example:
   ```bash
-  python flash_examples/finetuning/classify_text.py
+  python flash_examples/finetuning/summarization.py 
   ```
 </details>
 
@@ -273,6 +235,7 @@ Flash has a TabularClassification task to tackle any tabular classification prob
   To illustrate, say we want to build a model to predict if a passenger survived on the Titanic.
 
   ```python
+  # import our libraries
   from pytorch_lightning.metrics.classification import Accuracy, Precision, Recall
   import flash
   from flash import download_data