Ecoder-focused fine tuning

 

Encoder-focused tasks, the fine-tuning process generally ignores decoders. Let’s elaborate on this in more detail.

Encoder-Focused Tasks

Encoder-focused tasks are typically those that require understanding and analyzing input text rather than generating new text. Examples include:

• Text classification (e.g., sentiment analysis)

• Named entity recognition (NER)

• Question answering (where the answer is extracted from the input text)

• Sentence similarity

Pretraining

During pretraining for models designed for encoder-focused tasks (like BERT), only the encoder is used. The encoder learns bidirectional representations of the text by predicting masked tokens and performing tasks like next sentence prediction.

Fine-tuning

For fine-tuning on encoder-focused tasks, the pretrained encoder is used as the foundation. Here’s how it works:

1. Task-Specific Layers:

• Task-specific layers are added on top of the pretrained encoder. These layers are typically shallow neural networks or even simple classifiers like a linear layer, depending on the complexity of the task.

2. Training Process:

• The entire model, including both the pretrained encoder and the newly added task-specific layers, is fine-tuned using a labeled dataset specific to the task. This means the parameters of the encoder are updated alongside the parameters of the task-specific layers during the fine-tuning process.

3. Ignoring the Decoder:

• Since the task only involves understanding the input text and not generating text, the decoder component is not needed. Models like BERT, which consist only of an encoder stack, do not have a decoder component to begin with. Therefore, the fine-tuning process does not involve a decoder.

Example: BERT for Sentiment Analysis

1. Pretraining:

• BERT’s encoder is pretrained using tasks like masked language modeling (MLM) and next sentence prediction (NSP).

2. Fine-tuning:

• For a sentiment analysis task, a classification layer (e.g., a linear layer) is added on top of the BERT encoder.

• The model is then fine-tuned on a labeled sentiment analysis dataset. During this process, the BERT encoder and the classification layer are trained together.

Summary

• Pretraining: The encoder learns general language representations.

• Fine-tuning: For encoder-focused tasks, only the encoder is fine-tuned along with additional task-specific layers. The decoder is not involved or needed.

Encoder-Decoder Models

In contrast, for tasks that require text generation (e.g., machine translation, text summarization), both the encoder and decoder are involved:

1. Pretraining:

• Both the encoder and decoder are pretrained (e.g., in models like T5 or BART).

2. Fine-tuning:

• Both components are fine-tuned together on the specific task, such as translating text from one language to another.

In summary, for encoder-focused tasks, the fine-tuning process indeed ignores the decoders, focusing solely on adapting the encoder to the specific task at hand.