When it comes to natural language processing (NLP) and machine learning, the world of models can be overwhelming. But understanding the difference between encoder, decoder, and encoder-decoder models can make it much easier to tackle a variety of tasks. Let's break them down in a human-friendly way!
Encoder Models: The Meaning Makers
Imagine you're reading a sentence, and you're trying to understand its meaning. Encoder models do something very similar—they take in a sequence (like a sentence) and transform it into a rich semantic representation. This representation captures the essence or meaning of the input.
For example, let’s say we feed the sentence "Sreeni bought a Tesla car" into an encoder. The encoder doesn’t just process each word individually; it understands the relationships between the words and generates a rich vector that represents the full meaning of the sentence. This vector, or embedding, is then used in a variety of tasks. You could use it for things like entity recognition (to identify "Sreeni" as a person, Tesla is Org and "car" as an object) or classification (to categorize the sentence into a specific type, such as “purchase activity”).
Please refer to below blog link to learn how BERT is used for encoding or embedding and then leveraged for sentiment analysis.
https://dev.to/sreeni5018/fine-tuning-bert-for-precise-sentiment-detection-in-blog-feedback-1bm0
Model Example:
- BERT (Bidirectional Encoder Representations from Transformers) is a well-known encoder model. It takes in the entire sentence and creates deep contextual embeddings, allowing it to understand the nuances of each word's meaning within the sentence.
Decoder Models: The Creators
Now, what if we want to generate a response instead of just understanding input? This is where decoder models come in. They're the storytellers, designed to take some input (often an embedding, like the one from an encoder) and generate new sequences of text.
For example, let's say you input the word "Once upon a time" into a decoder model. The model doesn’t just stop there. It will generate the rest of the story, perhaps continuing with “there was a programmer named Sreeni who built amazing AI tools.” Decoder models are fantastic for tasks that involve text generation, like chatbots, story generators, or even code completion!
Model Example:
- GPT (Generative Pretrained Transformer) is a powerful decoder model. It excels at generating coherent and contextually relevant text based on a given prompt.
Encoder-Decoder Models: The Transformers
If the encoder and decoder models are like understanding and generating text, then encoder-decoder models are the bridge between these two worlds. These models are designed for tasks that involve transforming an input sequence into a different output sequence. So, think of them as a full process, from understanding the input to generating a new form of output.
Let’s take machine translation as an example. If you want to translate “Hello, Sreeni” from English to Tamil or Hindi, an encoder-decoder model would first use the encoder to understand the meaning of the sentence. Then, the decoder would take that understanding and generate the translated output, “வணக்கம், ஸ்ரீனி” in Tamil or “नमस्ते, श्रीनी” in Hindi.
Model Example:
- T5 (Text-to-Text Transfer Transformer) is an encoder-decoder model that can perform a wide range of tasks, including translation, summarization, and text generation. It treats all tasks as converting input text into output text, making it extremely versatile.
Wrapping Up
Encoder models are perfect for extracting meaning and representations from text.
Example Model: BERT – Great for tasks like entity recognition and text classification.
Example: Understanding the sentence "Sreeni bought a Tesla car."Decoder models excel at generating text based on some input.
Example Model: GPT – Known for generating human-like text for dialogue, story generation, and more.
Example: Generating a continuation like “there was a programmer named Sreeni who built amazing AI tools.”Encoder-decoder models are ideal for transforming one sequence into another, like in translation or summarization.
Example Model: T5 – Great for tasks like machine translation, summarization, and question answering.
Example: Translating “Hello, Sreeni” into “வணக்கம், ஸ்ரீனி” (Tamil) or “नमस्ते, श्रीनी” (Hindi).
Each type of model plays a critical role in tasks involving text, making it possible to not only understand but also create meaningful outputs from data. It’s the backbone of a lot of today’s AI advancements, from translation apps to the AI you’re chatting with right now!
Isn’t it amazing how these models come together to create some truly smart tech?
Thanks
Sreeni Ramadorai
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