Encoder-Decoder Tool

Explore sequence-to-sequence models used in machine translation, text summarization, and conversation systems. Understand how encoders compress input and decoders generate output.

What is Encoder-Decoder?

Encoder-Decoder is a neural network architecture where an encoder processes input data into a compressed representation (context vector), and a decoder generates output from this representation. It's used for tasks like machine translation (English → French), text summarization, and chatbots.

Input Sequence

The quick brown fox jumps over the lazy dog.

Translation Example

Summarization Example

Chatbot Example

Code Explanation

Model Configuration

Architecture

RNN-based (LSTM/GRU)

Transformer-based

Use Attention Mechanism

Model Parameters

Embedding Size:

Hidden Size:

Number of Layers:

Task Type

Translation

Summarization

Chatbot/QA

Encoder-Decoder Architecture

Processing Sequence...

Encoding input and generating output

Neural Network Architecture

ENCODER

Context Vector

DECODER

Output & Analysis

Generated Output

Attention Visualization

Training Details

Decoded Output

Click "Encode & Decode" to generate output from the input sequence.

Input Stats

Length: 0 tokens

Words: 0

Output Stats

Length: 0 tokens

Compression: 0%

Attention Weights

Attention mechanism shows which input words the decoder focuses on when generating each output word. Darker colors indicate stronger attention.

Input/Output	The	quick	brown	fox	jumps
Le	0.85	0.05	0.02	0.01	0.01

Strong attention (0.7-1.0)

Medium attention (0.3-0.7)

Weak attention (0.1-0.3)

Little attention (0.0-0.1)

Model Training Information

Model Complexity

Total parameters in the encoder-decoder model:

2.4M

Training Epochs

Number of training iterations on sample data:

50

Inference Speed

Time to process input and generate output:

0.12s

Accuracy

Estimated accuracy on this task type:

87.5%

How Encoder-Decoder Works

Encoder: Processes input sequence word by word, creating a context vector that summarizes the entire input.
Context Vector: A fixed-length representation capturing the input's meaning.
Decoder: Generates output sequence step by step, using the context vector and previously generated words.
Attention: Allows decoder to focus on different parts of the input for each output step.

How to Add This Encoder-Decoder Tool to Your Blogger Site

Step 1: Copy All Code

Select all the code on this page (click and drag or press Ctrl+A then Ctrl+C). The entire page is a single HTML file.

Step 2: Create New Blog Post

In your Blogger dashboard, create a new post or edit an existing one where you want to add the tool.

Step 3: Switch to HTML Mode

Click the "HTML" button in the post editor to switch from Compose to HTML mode.

Step 4: Paste & Publish

Paste the copied code (Ctrl+V) into the HTML editor, then publish or update your post.

What is Encoder-Decoder Used For?

Encoder-Decoder architecture is fundamental to modern NLP applications: Machine Translation (Google Translate), Text Summarization (news articles → summaries), Chatbots & Virtual Assistants (Siri, Alexa, Google Assistant), Speech Recognition, Image Captioning (images → text descriptions), and Code Generation (natural language → code).

Encoder-Decoder Visualization Tool | Designed for Blogger | No Coding Knowledge Required

Sequence-to-Sequence Models & Neural Machine Translation