Have you ever seen how gmail predicts the next word or words while typing a mail or how google shows you relevant search suggestions when you type something in search bar or have you wondered how a state-of-the-art chatbot or digital assistant like Alexa or Siri work. All of these fall under the application of Natural Language Processing.
Language modeling is the task of predicting the next word or character in a document. This problem has immense application in NLP and can be utilized for more complex problems like text generation, text classification and question answering.
There are two common language modeling techniques involve:
- N-gram Language Models
- Neural Language Models
A model's language modeling capability is measured using cross-entropy and perplexity. Some datasets to evaluate language modeling are WikiText-103, One Billion Word, Text8, C4, among others.
To evaluate and compare different models the SuperGLUE benchmark is used.
Some of the State-of-the-art(Open Source) models in NLP:-
Let’s read on to the list of Top 7 leading language models for NLP:-
- BERT: BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications.
- RoBERTa: RoBERTa is a robustly optimized BERT pre-training approach that stands for robust optimized BERT pre-training approach. The researchers who brought it out believed BERT was “under-trained” & can be improved by following several changes while pre-training. RoBERTa uses dynamic masking, wherein for different Epochs different parts of the sentences are masked. This makes the model more robust.
- ALBERT: Albert stands for a light and the rest is burned now, it is a version of the transformer model BERT that optimizes for the number of model parameters (size of the model) in BERT. It optimizes model training and makes it faster than burned. ALBERT is a lot different from BERT, In BERT the embedding dimension is tied to the hidden layer size. Increasing hidden layer size becomes more difficult as it increases embedding size and thus the parameters. ALBERT shares all the parameters across layers to improve parameter efficiency. Authors of ALBERT claim that the NSP task on which BERT is trained along with MLM is easy. ALBERT uses a task where the model has to predict if sentences are coherent.
- XLNet: It is a generalized autoregressive pre-training method that enables permutations of the factorization order and overcomes the limitations of BERT, thanks to its autoregressive formulation. Furthermore, XLNet integrates ideas from Transformer-XL, the state-of-the-art auto-regressive model, into pre-training. Empirically XLNet outperforms BERT on 20 tasks often by a large margin and achieves the state-of-the-art results on 18 tasks. It includes questions answering natural language inference, sentiment analysis and document ranking. Unsupervised representation learning has been highly successful in the domain of natural language processing among them. Auto regressive language modeling and auto encoding have been the two most successful pre-training objectives. Auto regressive language marking seeks to estimate the probability distribution of a text corpus given a text sequence. Best of both AR language modeling and AE while avoiding their limitations. Maximizes the expected log likelihood of a sequence w.r.t all possible permutations of the factorization order. Also, it integrates methods from transformer XL.
- Open AI’s GPT 2: In addition to using supervised learning on task-specific datasets for tasks such as question answering, machine translation, reading comprehension, and summarization, other natural language processing tasks are also generally approached with supervised learning. In OpenAI’s GPT2, trained on a new dataset of millions of web pages called WebText, language models begin learning these tasks even without explicit supervision.
- ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately): ELECTRA is basically again a variant of BERT that helps us in performing fine-tune tasks a bit faster as compared to other variants. ELECTRA does two things, one it completely removes NSP (Next Sequence Prediction) as the research says that NSP is not adding much value to the training and hence it is completely removed. Second, In case of MLM (Mass Language Modeling), the electric pre-training answering to natural language inference, the electric pre-training objective is more efficient and leads to better performance than the masked, it is going with the idea of replacing tokens now.
- DeBERTa: DeBERTa is a new model architecture, decoding enhanced BERT with disentangled attention that improves BERT and RobertA models using two novel techniques. The first is the disentangled attention mechanism where each word is represented using two vectors that encode its content and position respectively. We can train all these models in several parallelism paradigms to enable model training across multiple GPUs, as well as a variety of model architecture and memory saving designs to help make it possible to train very large neural networks. Thinking of buying a Cloud GPU now? E2E Cloud can help you by providing AI accelerated Cloud GPUs at a cost 40% lower than hyperscalers.
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