The 6 Best Pre-Trained Models for Work and Business
The barrier to training an effective and reliable AI has significantly lowered thanks to the public release of many pre-trained models. With the pre-trained models, independent researchers and smaller businesses can streamline processes, enhance productivity, and gain valuable insights through the use of AI.
There are now many pre-trained models you’re able to use and fine-tune. Depending on your specific problem, you may want to use one model over another. So how do you know which pre-trained model to use?
To help you decide, here are some of the most popular pre-trained models you can use to boost your work and business productivity.
1. BERT (Bidirectional Encoder Representations from Transformers)
BERT is an encoder transformer that revolutionized natural language processing (NLP) with its self-attention mechanism. Unlike traditional recurrent neural networks (RNN) that process sentences one word after the other, BERT’s self-attention mechanism allows the model to weigh the importance of words in a sequence by computing attention scores between them.
BERT models have the capability to understand the deeper context in a sequence of words. This makes BERT models ideal for applications that require powerful contextual embedding that have strong performance across various NLP tasks such as text classification, named entity recognition, and question answering.
BERT models are typically large and require expensive hardware to train. So, although considered the best for many NLP applications, the downside to training BERT models is that the process is often expensive and time-consuming.
2. DistilBERT (Distilled BERT):
Looking to fine-tune a BERT model but don’t have the money or time required? DistilBERT is a distilled version of BERT that retains around 95% of its performance while only using half the number of parameters!
DistilBERT uses a teacher-student training approach where BERT is the teacher and DistilBERT is the student. The training process involves distilling the knowledge of the teacher to the student by training DistilBERT to mimic the behavior and the output probabilities BERT.
Due to the distillation process, DistilBERT does not have token-type embeddings, has reduced attention heads, and lesser feed-forward layers. This achieves a significantly smaller model size but sacrifices some performance.
Just like BERT, DistilBERT is best utilized in text classification, named entity recognition, text similarity and paraphrasing, question answering, and sentiment analysis. Using DistilBERT may not grant you the same level of accuracy as with BERT. However, using DistilBERT allows you to fine-tune your model much faster while spending less on training.
3. GPT (Generative Pre-trained Transformer)
Do you need something to help you generate content, give suggestions, or summarize text? GPT is OpenAI’s pre-trained model that produces coherent and contextually relevant texts.
Unlike BERT, which is designed under the encoder transformer architecture, GPT is designed as a decoder transformer. This allows GPT to be excellent at predicting the next words based on the context of the previous sequence. Trained on vast amounts of text on the internet, GPT learned patterns and relationships between words and sentences. This allows GPT to know which words are most appropriate to use in a certain scenario. Being a popular pre-trained model, there areadvanced tools such as AutoGPTthat you can use to benefit your work and business.
Although great at mimicking human language, GPT has no basis in facts besides the data set used to train the model. Since it only cares if it generates words that make sense based on the context of previous words, it may provide incorrect, made-up, or non-factual responses from time to time. Another problem you might have fine-tuning GPT is that OpenAI only allows access via an API. So, whether you want to fine-tune GPT orjust keep training ChatGPT with your custom data, you will need to pay for an API key.
4. T5 (Text-to-Text Transfer Transformer)
T5 is a highly versatile NLP model that combines both encoder and decoder architectures to tackle a wide range of NLP tasks. T5 can be used for text classification, summarization, translation, question answering, and sentiment analysis.
With T5 having small, base, and large model sizes, you can get an encoder-decoder transformer model that better fits your needs in terms of performance, accuracy, training time, and cost of fine-tuning. T5 models are best utilized when you can only implement one model for your NLP task applications. However, if you must have the best NLP performance, you may want to use a separate model for encoding and decoding tasks.
5. ResNet (Residual Neural Network)
Looking for a model that can complete computer vision tasks? ResNet is a deep learning model designed under the Convolutional Neural Network Architecture (CNN) that’s useful for computer vision tasks such as image recognition, object detection, and semantic segmentation. With ResNet being a popular pre-trained model, it’s possible to find fine-tuned models, then usetransfer learning for faster model training.
ResNet works by first understanding the difference between the input and output, also known as “residuals.” After the residuals are identified, ResNet focuses on figuring out what is most likely between those inputs and outputs. By training ResNet on a large data set, the model learned complex patterns and features and can understand what objects normally look like, making ResNet excellent at filling the in-betweens of the input and output of an image.
Since ResNet only develops its understanding based on the dataset given, overfitting might be an issue. This means if the data set for a specific subject was insufficient, ResNet may wrongly identify a subject. So, if you were to use a ResNet model, you would need to fine-tune the model with a substantial data set to ensure reliability.
6. VGGNet (Visual Geometry Group Network)
VGGNet is another popular computer vision model that is easier to understand and implement than ResNet. Although less powerful, VGGNet uses a more straightforward approach than ResNet, utilizing a uniform architecture that breaks images into smaller pieces and then gradually learns its features.
With this simpler method of analyzing images, VGGNet is easier to understand, implement, and modify, even for relatively new researchers or practitioners of deep learning. You may also want to use VGGNet over ResNet if you have a limited dataset and resources and would like to fine-tune the model to be more effective in a specific area.
Numerous Other Pre-Trained Models Are Available
Hopefully, you now have a better idea of what pre-trained models you can use for your project. The models discussed are some of the most popular in terms of their respective fields. Keep in mind that there are many other pre-trained models publicly available in deep learning libraries, such as TensorFlow Hub and PyTorch.
Also, you don’t have to stick to only one pre-trained model. As long as you have the resources and time, you can always implement multiple pre-trained models that benefit your application.
GPT isn’t the only language processing model in town.
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