NLP Transfer Learning, Top 5 Models And How To Adapt Them With Fine-Tuning

Transfer learning is explained, and the advantages and disadvantages are summed up. Types of transfer learning in NLP are summed up, and a list of the top models commonly used for transfer learning is provided. We explain how you can adapt a model with transfer learning and what applications can benefit from this technique.

What is transfer learning in NLP?

Transfer learning is a machine learning method in which a model trained on one activity is repurposed for another activity related to the first. Transfer learning is helpful in natural language processing (NLP) when you want to improve performance on another language or NLP task by leveraging the knowledge learned by a model on one language or NLP task. This can be accomplished by leveraging the knowledge learned by the model on the first language or NLP task.

This can be especially helpful when you have a limited amount of data available for the task you are interested in. You can use the weights and biases learned by a model trained on a larger dataset as a starting point for your model.

For example, if you have limited data available when you are just starting on a new task, you can use the weights and biases learned by another model. This way, you can use the knowledge gained from the larger dataset as a starting point for your model.

Transfer learning lets you move knowledge from one task to another.

Using transfer learning has advantages and disadvantages. We will cover these now so that you can better grasp whether the technique is right for you.

Advantages

1. Improve performance: Transfer learning helps improve performance by letting a model use what it has learned from a related task to do new tasks better.
2. Reduced training time: Since the model can start with pre-trained weights and biases, transfer learning can reduce the amount of data and computational resources needed to train a new model from scratch.
3. Less data needed: Since the model can learn from the larger dataset used to train the original model, transfer learning can be helpful when only a limited amount of data is available for a new task.

Disadvantages

1. Limited adaptability: You might need help to modify a pre-trained model to suit your needs better, and it might not be a good fit for your new task.
2. Task mismatch: If the original task and the new task are very different, the model may not be able to transfer its knowledge, which would result in poor performance on the new task.
3. Dependence on the quality of the original model: The performance of your transferred model will depend on the quality of the original model. If the original model were poorly designed or trained on flawed data, the transferred model would also be of poor quality.

Types of transfer learning in NLP

Natural language processing (NLP) can make use of a variety of transfer learning techniques, including:

1. Feature-based transfer learning: The objective of feature-based transfer learning is to apply the features learned from a pre-trained model to a new task. To do this, a model that has already been trained can be used to pull out features from the training data. These features can then be used to train a new model.
2. Fine-tuning: A pre-trained model can be “fine-tuned” by changing its hyperparameters and architecture to fit the requirements of a fresh task better. This can be achieved by training the model on a small sample of the new task’s labelled data while maintaining most of the model’s weights at their current values.
3. Multi-task learning: A single model is trained to carry out several connected tasks simultaneously in multi-task learning. This can be helpful when the tasks are connected and enough data is available for each task.
4. Unsupervised transfer learning: In the absence of labelled training data, unsupervised transfer learning involves applying the knowledge acquired by a previously trained model to a new task. To accomplish this, either create synthetic labelled data using the pre-trained model or use the model to pre-train a different model on a related task.

What models are available in NLP for transfer learning?

Now that you have considered the pros and cons of transfer learning, the question is whether the technique is right for you. This largely depends on the problem you are trying to solve and how closely it relates to the existing pre-trained models.

These are the most commonly used pe-trained models for NLP. Check them out.

1. BERT (Bidirectional Encoder Representations from Transformers): BERT has attained state-of-the-art performance on various NLP tasks. It is a transformer-based model. It can learn the contextual relationships between words in a sentence because it is trained to predict masked words in sentences.
2. GPT (Generative Pre-trained Transformer): GPT is a transformer-based model that can produce text that resembles a human being. It was trained on a sizable dataset of unstructured text. It can be honed to perform various language generation tasks, including question answering, summarising, and machine translation.
3. ELMo (Embeddings from Language Models): The language model ELMo creates contextualised word embeddings that can be used as input for subsequent tasks. Sentiment analysis and named entity recognition are just two of the NLP tasks where it has been demonstrated to enhance performance.
4. ULMFiT (Universal Language Model Fine-tuning): A technique known as “gradual unfreezing” is used in the ULMFiT method to fine-tune a language model for a particular task. This way, the model can learn task-specific language features while keeping the general language skills it learned during pre-training.
5. Robustly Optimised BERT or RoBERTa: This BERT variant performs better on a range of NLP tasks because it was trained over a larger dataset and for a more extended time.

How to adapt a model using fine-tuning

Now that you have chosen a pre-trained model to work with, it’s time to adapt your model to your task. This process is known as “fine-tuning” rather than “model training.”

Fine-tuning involves adjusting the model’s hyperparameters and architecture better to suit the specific characteristics of your new task. You can accomplish this by:

1. Adding or removing layers: You can increase the model’s capacity by adding layers or removing layers to make it smaller and less prone to overfitting.
2. Changing the learning rate: During training, the learning rate controls how quickly the model updates its weights and biases. The model may learn more quickly with a higher learning rate, but overfitting is also more likely.
3. Changing the optimiser: Each optimiser has unique properties and may perform better or worse depending on the task. You can try different optimisers to see which works best for your task.
4. Changing the training data: You can try out different pre-processing or augmentation techniques, such as lemmatisation, stemming, or adding artificial examples to the dataset using back-translation techniques.

When fine-tuning a transfer model, caution is advised because it is simple to overfit the model to your training data if you are not careful. Therefore, as you make changes, it’s a good idea to keep an eye on the model’s performance on a validation set to ensure it is manageable.

What impact has transfer learning had on NLP?

Natural language processing (NLP) has dramatically benefited from transfer learning because it enables practitioners to use the information gained from pre-trained models on large datasets to enhance performance on new tasks with little to no data. Because of this, the performance of many NLP tasks, such as sentiment analysis, named entity recognition, and machine translation, has improved much.

The ability to build models that can handle a variety of languages and tasks without having to train a new model from scratch for each task and language is one of the main benefits of transfer learning in NLP. Because of this, the data and computing power needed to train new models have decreased significantly. As a result, people can build and deploy NLP systems faster and for less money.

Since transfer learning allows practitioners with limited resources to build high-quality models by starting with a pre-trained model and customising it to their particular task, it has also democratised the field of NLP. This has stimulated new NLP applications and raised the bar for innovation.

Applications of transfer learning in NLP

There are many uses for transfer learning in natural language processing (NLP). Examples of typical applications include:

1. Text classification: Transfer learning can improve the effectiveness of text classification models like spam detection, sentiment analysis, and topic classification.
2. Language translation: Experts can make high-quality translation systems without using large parallel datasets because they can fine-tune pre-trained models.
3. Text generation: To improve language models for text generation tasks like machine translation, summarisation, and question answering, transfer learning can be used.
4. Sentiment analysis: By utilising the information gained from a pre-trained model’s experience on a related task, transfer learning can enhance sentiment analysis models’ performance.
5. Named entity recognition: Transfer learning can make named entity recognition models work better by starting with a model that has already been trained and then fine-tuning it to the specifics of the target domain.
6. Part-of-speech tagging: Transfer learning can improve the performance of part-of-speech tagging models by using the knowledge learned by a model already trained on a similar task.

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Want to learn how to fine-tune an open-source large language model (LLM)? Follow our tutorial on how to apply transfer learning on LLMs.

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