Language models have revolutionized the field of natural language processing (NLP) and have become an essential tool for a wide range of applications. These models are trained on large amounts of text data and can perform a variety of tasks such as language translation, text generation, question answering, and sentiment analysis.
We took the time to list some of the most popular language models and their capabilities so you won’t have to.
| Language Model | Description | Training Data Format and Quantity | Capabilities |
|---|---|---|---|
| BERT | Bidirectional Encoder Representations from Transformers is a transformer-based model that uses an attention mechanism to understand the context of words in a sentence. | Pre-trained on a large corpus of text data in unstructured format, including books, articles, and websites. Quantity of data is estimated around 3.3 Billion words | Fine-tuned for various NLP tasks such as question answering, sentiment analysis, and named entity recognition. |
| GPT-2 | Generative Pre-trained Transformer 2 is a transformer-based model that uses an attention mechanism to generate human-like text. | Pre-trained on a massive amount of text data in unstructured format, including books, articles, and websites. Quantity of data is estimated around 40 GB of text data | Fine-tuned for various NLP tasks such as language translation, question answering, and text generation |
| RoBERTa | Robustly Optimized BERT Pre-training is an optimized version of BERT, which is trained on a larger dataset and for a longer time. | Pre-trained on a large corpus of text data in unstructured format, including books, articles, and websites. Quantity of data is estimated around 160 GB of text data | Fine-tuned for various NLP tasks such as question answering, sentiment analysis, and named entity recognition. |
| T5 | Text-to-Text Transfer Transformer is a transformer-based model trained on a diverse range of text-to-text transfer tasks. | Pre-trained on a diverse range of text-to-text transfer tasks in structured format, such as question answering, translation, summarization and more. Quantity of data is estimated around 11 GB of text data | Fine-tuned for various NLP tasks such as language translation, summarization, and text generation. |
| ULMFiT | Universal Language Model Fine-tuning is a pre-trained model that can be fine-tuned to a wide range of NLP tasks by using a small amount of task-specific data. | Pre-trained on a large corpus of text data in unstructured format, including books, articles, and websites. Quantity of data is estimated around 600 GB of text data | Fine-tuned for various NLP tasks such as text classification, language modeling and more. |
| XLNet | XLNet is a transformer-based model that is trained to predict all the tokens in a text by maximizing the likelihood of the permutations of the input sequence rather than just the original order. | Pre-trained on a large corpus of text data in unstructured format, including books, articles, and websites. Quantity of data is estimated around 32 TB of text data | Fine-tuned for various NLP tasks such as text classification, language modeling and more |
| ALBERT | A Lite version of BERT, which uses parameter sharing techniques to reduce the number of parameters and increase the training speed. | Pre-trained on a large corpus of text data in unstructured format, including books, articles, and websites. Quantity of data is estimated around 2.5 Billion words | Fine-tuned for various NLP tasks such as question answering, sentiment analysis, and named entity recognition. |
| ELMO | Embeddings from Language Models is a deep contextualized word representation that models both the complex characteristics of word use with multiple pre-trained layers. | Pre-trained on a large corpus of text data in unstructured format, including books, articles, and websites. Quantity of data is estimated around 5.5 Billion words | Fine-tuned for various NLP tasks such as text classification, question answering, and named entity recognition. |
| OpenAI GPT-3 | Generative Pre-trained Transformer 3 is a transformer-based model that uses an attention mechanism to generate human-like text. | Pre-trained on a massive amount of text data in unstructured format, including books, articles, and websites. Quantity of data is estimated around 570 GB of text data | Fine-tuned for various NLP tasks such as language translation, question answering, text generation, and even coding and question-answering in specific domains like medicine and law. |
| CTRL | Conditional Transformer Language Models is a transformer-based model that uses a conditional mechanism to generate text based on a given context or prompt. | Pre-trained on a diverse range of text data in unstructured format, including books, articles, and websites. Quantity of data is estimated around 45 GB of text data | Fine-tuned for various NLP tasks such as language generation, text summarization, and dialogue generation. |
| Megatron | A transformer-based model trained on a massive amount of text data. It is designed to scale to billions of parameters and can be fine-tuned for various NLP tasks such as language translation, text summarization, and named entity recognition. | Pre-trained on a massive amount of text data in unstructured format, including books, articles, and websites. Quantity of data is estimated around 45 TB of text data | Fine-tuned for various NLP tasks such as language translation, summarization, and named entity recognition |
