and found that using a learning rate of 5e-5, Linear Warmup Scheduler with 200 warmup steps, AdamW optimizer, total 5 epochs (more than 5 resulted in overfitting), gradient_accumulation_steps of 32 and max_grad_norm of 1 seems to be the best for both GPT and GPT-2 models. More testing is needed to identify optimial configurations for it.
All residual addition and normalization layers are omitted.
They are different in the embedding dimensionality, key, query, value vector’s dimensionality, number of attention layer in each decoder block, and number of decoder blocks in the model. We will thus only expand the search with sentence prefixes “I eat hamburger” and “I eat cake”.
The actual structure of decoder block consists of one attention layer only. From the figures above, we can now know that P( w | “I disapprove of what you say, but”) will be affected by the word “but” the most, followed by “what”, then “I”. The same sentence prefix will lead to the same sentence. A language model is a probabilistic model that predicts the next token in a sequence given the tokens that precede it.
For example, given a language model of English, we can ask what the probability is of seeing “All roads lead to Rome” in English.We could also expect that the probability of seeing grammatically wrong or nonsensical sentences, such as “jump hamburger I,” will be much lower than grammatically correct and more meaningful sentences, such as “I eat hamburger.”Let’s pull in some mathematical notation to help describe a language model.P(w1, w2, …, wn) means the probability of having the sentence “w1 w2 … wn”.Notice that language model is a probability distribution instead of just a probability. In our case, we are using the GPT-2 model to learn the language model. To quickly try GPT-2 on article generation, we could use Huggingface Transformers. Until now, we’ve discussed how output word embeddings are computed from input word embeddings.Input word embeddings are vectors, and the first steps of the transformation is to create even more vectors from those input word embeddings.
Foreword: This article was written as Oursky Skylab.ai Team recently completed an article generation project for a startups client, and we want to share the technique we used in the project. Discuss how to use language modeling to generate article.
This noise vector will be added to the input word embedding with the weight specified in σi.
When you want machine learning to convey the meaning of a text, it can do one of two things: rephrase the information, or just show you the most important parts of the content.
We also had a brief introduction to GPT-2 model and some of its internal workings. One attention layer includes multiple attention heads and the. GPT-2 includes not just one decoder block, but a chain of it.
We also saw how to use Huggingface Transformers in applying the GPT-2 model in text generation. To avoid blowing up and shrinking the output embedding, the sum of attention values need to be to 1.This implies that for the first word, its output embedding will be equal to its value vector; for example, Ioutput is equal to Ivalue.Each attention value xAyis computed by taking the dot product between the key vector of x and query vector of y; scaling down the dot product with the square root of the dimension of the key vector; and finally, taking the softmax to ensure the related attention values are summing up to 1, as shown below:xAy = softmax(xkeyT yquery / sqrt(k)), where k is the dimension of key vector.To recap, we should now know how output embedding is computed as the weighted sum of value vectors of the current and previous words. So, to increase the batch size, I used the idea of accumulating gradients for n number of steps before updating the weights, where n will be our batch size. This is in order to reshape the resultant P(w | context) by dividing each logit value by t before applying the softmax function. The words on the left are the output, and those on the right are the input.
It gives lots of comparison among texts generated using different approaches (beam search, top-k sampling, nucleus sampling, etc) and human-written text measured by different metrics.
Its value is computed by:eatoutput = IAeat Ivalue +eatAeat eatvalueHere, IAeat and eatAeat are the attention values. Clearly, we cannot check all those sentences’ probability within a reasonable time, even with a fast computer.Instead of constructing all possible sentences, we could instead just track top-N partial sentences. A cleaned and tokenized version can be found here $$. For more info, you can take a look at the official paper or OpenAI’s blog on GPT2. We will keep repeating the expansion and pick best-N procedure until we have a sentence with desired length.
The Seq2Seq architecture with RNNs or Transformers is quite popular for difficult natural language processing tasks, like machine translation or text summarization. In this article, I will try to: Explain what a language model is. Since this approach needs the minimum amount of data, it can be applied in various other narrow domains and low-resource languages. To make this a more computationally-efficient experiment, I did not train the model on the complete dataset. knowing what the previous words are.As P(w | context) is a probability distribution and could ask P(apple | context), P(orange | context) or any other words in the English dictionary, this means we could use P(w | context) to somehow predict what the next word is if the sentence goes on.
As we mentioned in the beginning, we will use the neural network called GPT-2 model from OpenAI to estimate the language model.GPT-2 is a Transformer-based model trained for language modelling. Also, factual inaccuracy and abstractiveness of the summaries decreases with large models, which might have been happening because of the increased memory abilities of larger models. By multiplying the input word embedding with these three matrices, we will get the corresponding key, query, and value vector of the corresponding input word. Notify me of follow-up comments by email. Below is my train function, and you can find the complete training script here: Most of the code in the above train function is self-explanatory. As the weights should sum up to 1, we will also take the softmax on the dot product. When the beam width is equal to the size of the dictionary, beam search becomes exhaustive search. One interesting fact we could see here is, most of the time, the first word is paid the most attention.
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