If you’ve ever taken a course, you know that the first week is always full of introductory activities and lectures. (If you haven’t taken a course, just think about any lengthy class you’ve ever sat through.) So before you even get to the “fun” stuff, you are already plowing through information about the course’s syllabus, what to bring to class, how to prepare for the course, the grading policy, and more. Once you finally get to the course’s main content, you might be too exhausted to really learn it.
Pre-Training NLP Assignment Help
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What is Pre-Training?
Pre-training in AI means building parameters based on one task to use them in subsequent tasks. It is used to understand new knowledge and perform new tasks by transferring and reusing old knowledge from the past.
A pre-training approach mimics the way humans make sense of new information. In other words, it is initializing the model parameters of a new task using model parameters of a task that has been previously learned. Using old experience, new models can perform new tasks successfully rather than starting from scratch. You can take advantage of our help services to avoid learning everything from scratch.
Best Pre-Training Models
GPT-3 from OpenAI
OpenAI’s GPT-3 is a controversial pre-trained model developed after GPT and GPT-2. It has 175 billion language parameters compared to previous non-sparse language models, which has a ten times larger set of parameters. Many NLP datasets have been used to train the model, including tasks like translation, answering questions, and word unscrambling; these tasks require on-the-fly reasoning. The recent advancements in its capabilities have led to its use in producing news articles and even generating code that is helping developers build ML applications. Many text prediction models are currently available, but GPT-3 is the largest and has impressive capabilities.
In 2018, Google announced the beta release of Bidirectional Encoder Representation from Transformers. A single Cloud TPU or a single GPU can train any question answering model in about 30 minutes. The release includes 11 NLP tasks, including Stanford questions, considered one of the most competitive. The method has been successfully used to pre-train deep neural networks using 2,500 million words from Wikipedia and 800 million from the Book Corpus. The accuracy of BERT was reported to be 93.2%, surpassing previous results.
CodBERT from Microsoft
The CodeBERT platform, named after the BERT framework from Google, is based on a bidirectional multilayer neural architecture. This model can perform various tasks, such as code searching and documentation generation, both in natural language and in programming languages. Also, CodeBERT’s performance was excellent for code-searching and documentation generation when the model parameters were fine-tuned for natural language tasks. In addition, this model was trained on 2.1 million bimodal and 6.4 million unimodal code points from the Github Code Repositories.
A computational model known as ELMo represents each word’s syntax, semantics, and linguistic context. Allen NLP has developed a model based on deep bi-directional models pre-trained on a large corpus of text. The ELMo model can be easily integrated into existing models, improving the capabilities across vast NLP applications, such as answering questions and sentiment analysis.
Word2vec is a tool developed by Google for creating static word embeddings. Word2vec is a method that represents word representations from a corpus C in a distributed manner. Using Word2Vec, an algorithm extracts a vector representation of each word from a corpus of text. With word2vec, a text corpus is an input, and a vector set of words is produced. A vocabulary is built from the training data, following which a vector representation is learned. Many language processing and machine learning applications use the resulting word vector files for feature extraction.
In Google’s XLNet, functions are learned from bidirectional contexts using an autoregressive algorithm. This predicts what events will happen based on a model trained on Transformer-XL. Besides performing text classification, sentiment analysis, and answer questions using NLP, it has often outperformed BERT in many NLP tasks. A recent study revealed that XLNet was superior to BERT in 20 tasks, including SquaD, GLUE, and RACE. It does not have the pre-train fine-tuning discrepancy of BERT, which eliminates the assumption of independence.
Google ALBERT is an upgrade of BERT’s deep-learning NLP capability, a model that performed well on 12 NLP benchmarks, including the competitive SQuAD v2.0 and SAT-style comprehension RACE benchmark. A free, open-source version of the model has been released based on TensorFlow and comes with ready-to-use representation models for many languages. Furthermore, it uses 89% fewer parameters than BERT yet achieves an average accuracy of 80.1%. The model uses factorization and parameter sharing in the hidden layer to reduce its size.
The Facebook RoBERTa system is built on BERT’s language masking strategy and optimized for pre-training a self-supervised NLP system. An intentionally hidden section of text within an unannotated language example has been programmed into the model to predict it. A key hyperparameter in the RoBERTa model is modified to improve its masked language modelling ability, enhancing downstream performance. Furthermore, RoBERTa is being trained over a longer period than BERT, since the researchers want to use more data. The researchers trained the model using NALP data sets along with public news articles.
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This article will explain the first GPT model developed by Open AI. GPT is a 12 layer 12 Attention head transformer decoder that explores how to take advantage of massive unlabeled text data sets to fine-tune them on limited supervised learning data sets. Some of the interesting contributions of the GPT model are the input transformations for test-specific fine-tuning and keeping language modeling as a part of the fine-tuning loss function. They also explore pre-training on the book’s corpus dataset, which requires longer-range context modeling than the one billion word benchmark dataset. This article will also give a quick description of the 12 supervised learning tasks GPT is fine-tuned on, such as Natural Language Inference, Multiple-Choice Style Question Answering, Semantic Similarity, and Text Classification.
This article will explain the first GPT model presented in the paper, Improving Language Understanding by generative pre-training developed by research scientists at Open AI GPT was developed to take advantage of Semi-Supervised Learning and Natural Language Processing. Semi-Supervised Learning describes the learning setting in which you have a massive, unlabeled data set and a relatively smaller label data set. This is especially evident in natural language processing because you can get a ridiculously large text data set from the Internet, such as Wikipedia, or doing things like the Common Crawl Corpus. But labeling data for tasks like question answering or semantic similarity takes much longer and requires significant manual effort. In GPT, the authors pre-train the model with the book’s corpus dataset; this dataset contains 7,000 unpublished books from various genres. This is one of the most interesting details about the GPT paper because they trade on this book’s corpus language, bottling task, and previous language models frequently. We’re looking at this one billion word benchmark.
So this image taken from a recent blog post unveiling deep mines New PG 19 data set illustrates how different data sets for this pre-training task language modeling have different long-range context modeling requirements.
This significantly impacts the pre-training performance when you then have the sequence model like a transformer or an LSTM. The one billion word benchmark requires an average context to do the language modeling task of about 27 words. So the transformer isn’t practicing that long-range modeling as much as it does on this book’s corpus dataset.
Fine Tuning Strategy
This slide describes a Fine-Tuning Strategy used in the GPT model to go from the pre-training language modeling task to fine-tuning on a different classification task and other tasks used to evaluate the model downstream supervised learning tasks. So first, you have this language modeling task, which is where you have this context of size K, and you’re iteratively predicting the next token in the sequence. So this works as you take the input, as this weight embedding of the tokens, plus the position embedding in the transformer. This WP denotes the position embedding matrix of We denote the text the token embedding matrix. So then you pass this input into the paper, they use 12 transformer blocks, and then you have the output Is the softmax distribution over that original embedding matrix for the tokens.
Then in the supervised learning tasks, you’re predicting the class label given the input sequence. So, in this case, you have the final output after it goes through these 12 transformer blocks is softmax between this final representation and then the weight matrix for the number of labels in the classification task. So one of the most interesting characteristics of GPT is the way they keep doing the language modeling, the pre-training task in the fine-tuning of the classification problem. So they have this lambda parameter that weights the loss as the model is fine-tuned. It’s also still doing text prediction as well as the new supervised learning task.
Task-Specific Input Transformations
One of the original paper’s key contributions is the way they do task-specific input transformations so that when they’re doing the supervised learning task, it has a similar input representation as the pre-training predicts the next Token language modeling task. So they introduce special tokens like a dollar sign for the delimiter between sentences, and the entailment, similarity, and multiple-choice question answering kind of task is all done so that the input for these supervisory tasks resembles the language modeling task input.
And these inputs are also well suited to additionally continue the language modeling task as an auxiliary objective. You do the same kind of iterative masking as you predict the context and predict the delimiter then predict the first answer in this kind of format, so it’s interesting to look at and see the input representation is exactly how they do this, such that you can have a smooth transition from the pre-training language modeling task into these different tasks of like text classification, entailment, semantic similarity, and then the question-answering tasks.
In the GPT model, they use a transformer decoder. Saidy here is that we have this attention over the inputs, and you don’t apply the same attention over the encoder. So in Transformer Decoder, you have the right half of the original transformer architecture, and you don’t do this encoding of the sequence into the decoder. Rather, it is just this part of the transformer.
Tasks Tested – Natural Language Inference
These are some of the tasks tested by supervised Learning with the original pre-training, with the GPT transformer decoder model. So the first is natural language inference, so the idea here is that you have this premise, and then you have a hypothesis, and you label how they relate to each other.
So you see something like, “yes now you know if everybody like in August when everybody’s on vacation or something, we can dress a little more casual or and then the hypothesis is that “August is a blackout month for vacations in the company.” So this is a contradiction because the two sentences don’t relate to each other. And the other is that “At the other end of Pennsylvania Avenue, people begin to line up for White House tours.” And then the hypothesis is that “people form the line at the end of Pennsylvania Avenue.” And this is an entailment because the two are related. So it’s an interesting task that requires this kind of language understanding to understand the relationship between the premise and the hypothesis.
Task Tested – Question Answering
The next task tested is question answering, and they use the race data set different from the standard question-answering dataset or the squad data set. So the way they format this is like a multiple-choice Question Answering, so it’s different from how to say the Bert model does question answering, how it traverses the passage, then Labels the answer within the passage. What this is doing is it’s looking at these different potential answers for the question, like “the girl handed the letter back to the mailman because” then these different possible answers that each gets passed separately as input representations to the transformer, and they’re aggregated in this final linear layer.
So all the representations that come out of each sequence from the transformer are combined with these additional linear classification letters.
Task Tested – Semantic Similarity
The next task tested is semantic similarity. A great example of this is the core data set question pairs, seeing if people ask the same questions repeatedly on Quora. So one example of similar questions is “Should I learn Python or Java first?”, is asking the same thing semantically as “if I had to choose between learning Java and Python, what should I choose to learn first?”. You see how this task requires this kind of a language understanding, and it’s difficult to parse it and do some term frequency, inverse document frequency, or some just overlapping Ingrams to tell if they have the same semantic meaning in the question.
Task Tested – Classification
In this case, the GPT model takes in the sentence, and then it labels it as being grammatically correct or incorrect.
So in total, the GPT model is evaluated with supervised Learning after doing the generative pre-training task on the books corpus data set on these 12 different data sets that are broadly categorized on tasks of natural language inference, which is where you have the premise and the hypothesis and your labeling the relationship between them. Question answering, which is formatted as a multiple-choice where you take in each of the different potential answer sequences and then have this extra modeling layer that will predict the correct answer from the representations form from each of the potential answer sequences.
Then you have sentence similarity, like the core question pairs, which is where you look at two questions or two sentences, and you tell if semantically they’re saying the same thing or not. And then you have Classification, things like the sentiment classification, like the Stanford Center, the Treebank. And then you have the grammatically correct or not classification task.
These ablations show the impact of some of the different factors of variation presented in the GPT model. The first is the number of layers transferred with this transfer learning task, As you go from pre-training on the language modeling on this 7,000 books corpus dataset into the different supervised learning tasks. So, for example, when you go from the training task into something like the core question pairs semantic similarity, you might decide to keep the six layers of the transformer decoder the same parameters from the training task and then randomly initialize weights for the next six transformers decoder blocks. And this shows that the more of the layers you keep from the training task, the better the model performs. The more layers you keep as you’re fine-tuning this model in the supervisory task, the better the overall accuracy and performance.
So similarly, this plot, it’s showing the effect of how many of these pre-training updates you do and how that impacts the downstream performance. So you see, again, the more steps you take with doing the pre-training on the books Corpus predict the next token, the better the model will perform on tasks like sentiment analysis and different other the 12 tasks that GPT model is then fine-tuned for.
This ablation shows the effects of different things like not doing the pre-training. You see a massive decrease in performance not using the auxiliary language model when you’re doing the fine-tuning. In this case, you see that the larger data sets like the core question pairs in the NYU natural language inference data set seem to benefit more by doing the auxiliary language multitask than when you have smaller data sets, which is an interesting thing. This auxiliary language modeling is describing when you’re fine-tuning the model, you’re still doing that Language modeling to predict the next token on the modified input sequence as done with that weighted lambda parameter.
And the last one is comparing the difference between LSTM, the same auxiliary language model setup, and the transformer. So you see that the transformer has a bigger prediction over a long range compared to the LSTM that is more of a short-range context modeling.
I hope from this article you’re able to take away how they use the generative pre-training task on that book’s corpus data set, which is interesting because it requires a longer context modeling than the previous one billion word benchmark and how they fine-tune this on the supervised learning task by having this interesting modification of the input sequence such that it resembles the pre-training task. And it’s also interesting to see how they keep that auxiliary language modeling objective when they’re doing the fine-tuning. I hope also you thought it was interesting to go through the task that they test these 12 different natural language processing tasks. And to get a better sense of what the GBT model is all about and the key ideas in this paper.