8 Of The Leading Language Models for NLP

Imagine a scenario, we have a model of a tangible world. What do you expect it to be able to do? Well, if it is a good model, it probably can predict what happens next given some description of "context", i.e., the current state of things.?

Historically most work in language modeling was focused on tasks like translation and speech recognition this is a famous result from google machine translation team around 2007 in which they found that they could continue to improve translation quality solely by increasing the amount of data used for their language models up to more than 200 billion tokens which was a lot of data at that time.?

More recently language modeling in the form of predicting the next word has become a user-facing application in itself with the rise of autocomplete and assistive writing technologies like smart compose improving a model’s ability to predict the next word directly helps end users and even more recently language models have shown potential to be general purpose NLP systems.?

The reason why language models play a crucial role in the development of NLP applications is that it is nevertheless time-consuming to build complex NLP language models from scratch. Transfer learning is a technique used to train models that perform a task using a dataset trained on another dataset. A new dataset is then used to repurpose the model for performing different NLP functions.?

Let’s read on to the list of Top 8 leading language models for NLP:

1. BERT:?BERT or Bidirectional Encoder Representation from Transformers is one of the most common languages used in computational skills in the last decade.?BERT? framework is a free and open-source deep learning structure that deals with Natural Language Processing (NLP). It is intended to assist computers in understanding ambiguity in words and establishing context from questionnaire data sets. If any given NLP approach comprehends natural spoken language, BERT can assist in picking the word out without securing any gap.

The models used in BERT have a sizable connection of labeled training data. The data scientists can label the data manually. It is a model group of transformers where encoders stack on each other. It is a precise language with an enlarged transformer masked language model in technical terminologies.?

1. 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.?
2. GPT-3:?It has been trending since its BETA release in July 2020, it’s believed to be one of the most significant models in the field of artificial intelligence. GPT-3 stands for generative pre-trained transformer, GPT-3 is a powerful 3rd generation language model developed by OpenAI and is in BETA testing as of july 2020.?

It can easily identify your problems and generate human-like texts very rapidly. GPT-3 is the latest example of a long line of pre-trained models like google bert, facebook's roberta & microsoft’s turing.?

Pre-trained models are large networks trained on massive data-sets usually without supervision. Soon after its release, the internet was flooded with text examples generated by GPT-3. Open-AI has been working on building AI models for sometime now and every breakthrough makes the news. GPT-3 seems to be a turning point in the field of AI.?

Specifications:

1. GPT-3 has 175 billion parameters: None of the previous language models have used such a large number of parameters.
2. It’s being trained with 45 terabytes of text data which includes sources from wikipedia, google books & coding tutorials.
3. 60% data for pre-training GPT-3 mo4del was taken for common crawl (They build & maintain crawl data that can be accessed and analyzed by anyone).
4. It has 96 decoder layers and is built on a system with 285K CPU cores, 10K GPUs and 400 GBPS network connectivity for each GPU server.

4)?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.?

5)?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.?

?6)?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.???

7)?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.?

8)?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.