Natural Language Processing (NLP) has become an essential area of research and development in Artificial Intelligence (AI) in recent years. NLP models have been designed to help computers understand, interpret, and generate human language. These models have been used in various applications, including machine translation, sentiment analysis, text summarization, speech recognition, and question-answering.
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As the demand for better and more efficient NLP models increases, researchers have been developing new models that can handle more complex tasks and produce more accurate results. In this context, we will discuss the top 20 leading NLP models that have achieved remarkable performance on various NLP benchmarks and are widely used in academic and industry research.
What are pre-trained models for NLP?
Pre-trained models for NLP are language models trained on massive amounts of text data to learn the underlying patterns and structures of human language. These models use unsupervised learning, where the model learns to predict the next word in a sentence given the previous words.
Pre-training a model involves feeding it with large amounts of text data, such as Wikipedia articles or news articles, and training it to learn the patterns and structures of human language.
Once pre-trained, these models can be fine-tuned for specific NLP tasks such as sentiment analysis, text classification, and machine translation. Fine-tuning involves training the pre-trained model on a smaller dataset specific to the task. This fine-tuning process allows the model to adapt to the nuances of the specific language domain and perform better on the targeted task.
Pre-trained models have become popular in recent years as they can significantly reduce the time and resources required to develop an NLP model from scratch. By leveraging the knowledge learned from pre-training, developers can fine-tune these models for their specific needs and achieve impressive results with minimal effort. As a result, pre-trained models have become the backbone of many NLP applications and have played a significant role in advancing the field of NLP.
What is transfer learning for pre-trained models in NLP?
Transfer learning is a powerful technique that allows you to use pre-trained models for NLP tasks with minimal training data. With transfer learning, you can take a pre-trained model and fine-tune it on your task rather than train a new model from scratch. This can save time and resources and often leads to better performance than training a model from scratch. Check out our tutorial on how to apply transfer learning to large language models (LLMs).
With transfer learning, you can take a pre-trained model and fine-tune it for your specific task.
Top 20 best NLP models
1. GPT-4 (Generative Pre-trained Transformer 4)
GPT-4 is a multimodal large language model created by OpenAI and the fourth in its GPT series. It was released on March 14, 2023, and has been made publicly available via ChatGPT Plus, with access to its commercial API being provided via a waitlist. It was trained to predict the next token and fine-tuned with reinforcement learning from human and AI feedback for human alignment and policy compliance.
GPT-4 improves ChatGPT but retains some of the same problems. It can take images and text as input, but OpenAI has declined to reveal technical details such as the model’s size.
2. GPT-3: Generative Pre-trained Transformer 3
GPT-3 is a massive NLP model that has revolutionized the field of NLP. It has a whopping 175 billion parameters, the highest number of parameters in any NLP model. GPT-3 can generate human-like responses to prompts, complete sentences, paragraphs, and even whole articles. Its pre-training allows it to perform various NLP tasks, including machine translation, question answering, and text summarization.
3. BERT: Bidirectional Encoder Representations from Transformers
BERT is a pre-trained NLP model widely used in various NLP tasks, such as sentiment analysis, question answering, and text classification. It generates contextualized word embeddings, meaning it can generate embeddings for words based on their context within a sentence. BERT is trained using a bidirectional transformer architecture that allows it to generate embeddings for both the left and right contexts of a word.
4. ELMO: Embeddings from Language Models
ELMO is a pre-trained NLP model that generates contextualized word embeddings. ELMO uses a bidirectional language model that captures the dependencies between words in both directions. It uses these dependencies to generate embeddings for each word based on its context within a sentence. ELMO has shown impressive results on a range of NLP tasks, including sentiment analysis, text classification, and question answering.
5. RoBERTa: Robustly Optimized BERT approach
RoBERTa is a variant of BERT trained on a larger text corpus with more advanced training techniques. RoBERTa has achieved state-of-the-art performance on many NLP benchmarks, including sentiment analysis, text classification, and question answering. Its training includes additional pre-processing steps that improve the model’s ability to understand and process natural language.
6. T5: Text-to-Text Transfer Transformer
T5 is a pre-trained NLP model developed by Google that can be fine-tuned for various tasks, including text generation and translation. T5 uses a transformer-based architecture that allows it to handle long text sequences. It has achieved state-of-the-art performance on several NLP tasks, including question-answering, summarization, and machine translation.
7. ALBERT: A Lite BERT
ALBERT is a smaller and faster version of BERT that maintains its performance on various NLP tasks. ALBERT achieves this by using advanced training techniques that reduce the number of parameters while maintaining the same level of performance as BERT.
8. XLNet: eXtreme Language understanding Network
XLNet is a pre-trained NLP model that uses an autoregressive method to generate contextualized representations. It has achieved state-of-the-art results on several NLP benchmarks, including text classification and question answering.
9. GPT-2: Generative Pre-trained Transformer 2
GPT-2 is an earlier version of GPT-3 that has fewer parameters but still achieves impressive results on several NLP tasks, including text generation and summarization.
10. ULMFiT: Universal Language Model Fine-tuning
ULMFiT is a pre-trained NLP model that can be fine-tuned for various downstream tasks, including text classification, sentiment analysis, and question answering. ULMFiT uses a transfer learning approach that allows it to learn the underlying structure of natural language.
11. DistilBERT: Distilled BERT
DistilBERT is a smaller and faster version of BERT that has been trained using advanced techniques that reduce the model’s size and computational requirements. Despite its smaller size, DistilBERT maintains its performance on several NLP tasks, including question answering and text classification.
12. ELECTRA: Efficiently Learning an Encoder that Classifies Token Replacements Accurately
ELECTRA is a pre-trained NLP model that has achieved state-of-the-art performance on several NLP benchmarks, including text classification, sentiment analysis, and question answering. ELECTRA is trained using a novel method that replaces a small subset of input tokens with synthetic tokens generated by another neural network, which improves its ability to capture and generate meaningful representations of natural language.
13. GPT: Generative Pre-trained Transformer
GPT is the predecessor to GPT-2 and GPT-3, which also uses a transformer-based architecture to generate human-like responses to prompts. GPT has fewer parameters than its successors, but it still achieves impressive results on several NLP tasks, including text generation and machine translation.
Cross-lingual Language Model pre-trained by Facebook AI Research is a pre-trained NLP model that can understand and generate text in multiple languages. XLM-RoBERTa achieves this by training on a diverse corpus of text from multiple languages and using advanced training techniques that improve its ability to understand and generate natural language across different languages.
15. UniLM: Universal Language Model
UniLM is a pre-trained NLP model that can be fine-tuned for various downstream tasks, including text classification, question answering, and text generation. UniLM uses a combination of both uni-directional and bi-directional transformers to capture both the left and right contexts of words.
16. MobileBERT: Mobile BERT
MobileBERT is a smaller and faster version of BERT that has been optimized for mobile devices. MobileBERT achieves this by reducing the number of parameters and using advanced techniques that improve its efficiency while maintaining its performance on several NLP tasks.
17. DeBERTa: Decoding-enhanced BERT with Disentangled Attention
DeBERTa is a pre-trained NLP model that uses disentangled attention mechanisms to improve its ability to generate meaningful representations of natural language. DeBERTa achieves state-of-the-art performance on several NLP benchmarks, including text classification, question answering, and sentiment analysis.
18. CTRL: Conditional Transformer Language Model
CTRL is a pre-trained NLP model that can generate text conditioned on a specific topic or context. CTRL achieves this by allowing the user to input a set of prompts that guide the model’s text generation, which makes it useful for generating text in specific domains.
19. GShard: Giant Switching Gated Hierarchical Attention for Multi-task and Large-scale Learning
GShard is a pre-trained NLP model that uses a hierarchical attention mechanism to generate contextualized representations of natural language. GShard is designed to scale up to handle massive amounts of data and perform multiple NLP tasks simultaneously.
20. Flair: A Framework for State-of-the-Art Natural Language Processing
Flair is a pre-trained NLP model that uses a combination of different neural network architectures to perform a wide range of NLP tasks, including text classification, sentiment analysis, and named entity recognition. Flair also allows users to train their own custom NLP models using its pre-trained embeddings and architectures.
Conclusion NLP models
The development of NLP models has revolutionized how computers process and understand human language. From GPT-4 and BERT to Flair, the top 20 NLP models that we discussed have shown impressive performance on various NLP tasks and have become the backbone of many real-world applications.
These models have enabled us to build chatbots, language translators, and sentiment analyzers, among many others, with greater accuracy and efficiency. As the demand for better and more efficient NLP models increases, we can expect to see even more powerful models being developed in the future. NLP will undoubtedly continue to play a vital role in shaping the future of AI and transforming the way we interact with machines.