In FebruaryOpenAI released a paper describing GPT-2, a AI-based text-generation model based on the Transformer architecture and trained on massive amounts of text all around the internet. From a text-generation perspective, the included demos were very impressive: the text is coherent over a long horizon, and grammatical syntax and punctuation are near-perfect.
At the same time, the Python code which allowed anyone to download the model albeit smaller versions out of concern the full model can be abused to mass-generate fake news and the TensorFlow code to load the downloaded model and generate predictions was open-sourced on GitHub.
I waited to see if anyone would make a tool to help streamline this finetuning and text generation workflow, a la textgenrnn which I had made for recurrent neural network-based text generation.
Months later, no one did. So I did it myself. Thanks to gptsimple and this Colaboratory Notebookyou can easily finetune GPT-2 on your own dataset with a simple function, and generate text to your own specifications! Like previous forms of text generatorsthe inputs are a sequence of tokens, and the outputs are the probability of the next token in the sequence, with these probabilities serving as weights for the AI to pick the next token in the sequence. The byte pair encodings are later decoded into readable text for human generation.
The pretrained GPT-2 models were trained on websites linked from Reddit.SinGAN: Generating Images and Animations in Google Colab
As a result, the model has a very strong grasp of the English language, allowing this knowledge to transfer to other datasets and perform well with only a minor amount of additional finetuning.
Due to the English bias in encoder construction, languages with non-Latin characters like Russian and CJK will perform poorly in finetuning. In order to better utilize gptsimple and showcase its features, I created my own Colaboratory Notebookwhich can be copied into your own Google account. Later in the notebook is gpt2. Expanding the Colaboratory sidebar reveals a UI that you can use to upload files. For example, the tinyshakespeare dataset 1MB provided with the original char-rnn implementation.
Now we can start finetuning! This finetuning cell loads the specified dataset and trains for the specified number of steps the default of 1, steps is enough to allow distinct text to emerge and takes about 45 minutes, but you can increase the number of steps if necessary. While the model is finetuning, the average training loss is output every-so-often to the cell. Run the gpt2.
You can then download the compressed model folder from Google Drive and run the model wherever you want. Likewise, you can use the gpt2. Speaking of generation, once you have a finetuned model, you can now generate custom text from it!
By default, the gpt2. You can download the generated file locally via the sidebar, and use those to easily save and share the generated texts. The notebook has many more functions as well, with more parameters and detailed explanations! A weakness of GPT-2 and other out-of-the-box AI text generators is that they are built for longform content, and keep on generating text until you hit the specified length.How this A. I became a communist. The Communist A. I was trained using GPT It read books by Marx, Fanon, Gramsci, Lenin and other revolutionary authors.
The project aims to see how deep GPT-2 can understand deep philosophical ideas and concepts.
The results were quite entertaining and promising as we witnessed the A. It was calling for a revolution whenever possible. While discussing the A. Follow this link if you wish to read more about The Communist A. We will use Google Drive to save our checkpoints a checkpoint is our last saved trained model. Once our trained model is saved we can load it whenever we want to generate both conditional and unconditional texts. Use the below code to connect your Google Drive:. It also lets us avoid using bash-code.
We will work with M model which is pretty decent. The reason we will work with that model vs the M or the M is the limited GPU memory available in Colab when training. With that being said we can use the M and M if we want to use the pre-trained GPT-2 without any fine-tuning or training on our corpus. I have my text in a Github repository, but you can replace the url variable with whatever link you need.
You can also manually upload your text files to Google Colab or if you have the texts in your Google Drive then just cd towards that folder. Now that the texts are downloaded, use the below code to merge multiple text files into one.
Ignore this code if you will only be using one text file. Now, the below is optional because they are needed if you want to use the M model and the M model. I have used the M without these but I recommend doing it anyway, especially the CUDA because your model will run faster and those might fix some random bugs that you might get.
Now install Cuda v9. Restart runtime and move back into the GPT2 folder. Now for the moment we have all been waiting for, fine-tuning the model. Copy the one-liner below and run it. The model will load the lastest checkpoint and train from there it seems that loading previously trained checkpoints and adding to it can lead you to run into memory problems with the M in Colab. You can also specify the number of batches and the learning rate.It took at least two AI Winters to survive.
The story is obvious: theoreticallyArtificial Intelligence as a concept was already here. But in practice, computational power was still like this:. AI research had still to wait for practical realization. Finally, in the s, every owner of a personal computer became able to do experiments with Machine and Deep Learning.
It took a good amount of time for me to set it up and to get it running. But the results were astonishing :. I was in love with AI. And I wanted to try out more. Then I discovered Colab Notebooks. And they changed my life. Google Colab Notebooks enable the democratization of Data Science. They allow it everybody — AI researcher, artist, data scientist et al. Just run the cells, change the parameters, values, and sources, and enjoy the diversity of AI. I want to share with you some of my favorite Notebooks.
Google Colab: Using GPU for Deep Learning
Try them out! You can find many great essays here at Towards Data Science about backgrounds and working with Colab Notebooks.
DeepDream visualizes pattern recognition, interpretation and iterative generation by Neural Networks. By increasing this creative interpretation you can produce dream-alike imagery.
12 Colab Notebooks that matter
Neural Networks act like our brain in the case of Pareidolia : it looks for familiar patterns, which derive from datasets they were trained on. The example above a screen from my presentation on the AI Meetup Frankfurt, November demonstrates how our brain recognizes a face in the rock formations of Cydonia region on Mars.
Things to try out:. Try to generate different patterns and to resize images octaves. Use more iterations. Fun fact:. Initially, DeepDream used to recognize in every pattern mostly dog faces. According to FastCompany, the Network was trained on….
Read more about my experiments with DeepDream. Trained on ImageNet at a now-humble x resolution this Network became a standard by its manifold generative abilities. In this notebook, you can generate samples from a long list of categories. The same notebook allows you to create interpolation between images.
This approach was — and is — mindblowing and innovative since only Surrealists and Abstract Artists were previously so courageous to combine incompatible things. Now you can do it with photorealistic results, for example, generate an interpolation between a Yorkshire terrier and a Space shuttle. Read also:. In this experiment, the Deep Learned systems examine two source images — and transfer their style: not only colors but also shapes and patterns.
There are many approaches to train AI on Artworks. You get interesting resultsit could even be possible to trace the original artworks the model was trained on. New art game, everybody? This model was trained on WikiART images.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. You may find the model. Thank you gwern. I was able to train a model using Chinese dataset over the default M model without problem. However, when I try to generate samples either conditional or unconditionalI got "FileNotFoundError: [Errno 2] No such file or directory" for encoder.
For English text, all you need to do is copy over those missing files from the M model directory, which will have them assuming you ran the download script as you must've if you did any retraining of M, of course.
Since finetuning doesn't affect the BPE encoding details, it is merely further training the Transformer model itself; so, the model still assumes the exact same encoding as for OA's M model, and the encoding is defined by those files.
Do you know how to use finetuning in Colab? That is, if there is a way to use it in Colab I don't know of any reason you couldn't do finetuning in Colab the main restriction I'm aware of is you only get something like 12 GPU-hours? But I have little familiarity with it or interest in setting up a notebook to do the finetuning.
Colab seems like a very restrictive tool compared to running on your own machine. Thank you!! On a heldout set? No idea. You'll have to code that yourself. I suppose one dirty hack would be to set the learning rate to zero and 'train' on a 'new' dataset and watch the averaged loss However, ever since I heard the news about gpt-2, I was with the AI in my head and I wanted to try it myself.
Although I don't know anything about how this works, I still got with the help of information I collected on the internet to use it in the Colab I think it's safer to use it, since this way I'm not in danger of screwing up something important in the process. Having said that, I have just one last question.
How can I "save" my modifications? I don't want to have to train the GPT-2 and lose everything when I turn off the computer, again having to spend two hours training, plus I would want to put more texts to train without having to restart the runtime ie, starting again from zero.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. That was all too remarkable.
It was not merely impressive, but it took me on a turning short cough, and then swelling and stiffening, and rising to be a nice man, and a man, and not at all strivenly, and wicked. The wonderful corner for echoes, and the echoes not being the echoes of footsteps that had their shameful imparted on the other side alone, that the time and tide waited for Lucie were sufficiently near the room, that to the utmost remaining of the time, even though assisted off other carriages, and were always ready and drove away that they should not hear themselves, Jerry heard no cry, and were as quick as in the morning if they had to pull up their heads and cidled away together as they could.
The farrier had not been in the barrier when he stopped, for the moment, and was as quick as they could make him. He was to roll up arms, to get the outer coat to and frolic. He could not have laid down his hand to do so without another rain of the summer drops on high, when he was requested to do so. But, the rain of the summer was very strong and few, and the rain of the autumn month afterwards was strong and warm by those intervals.
The storm in the west was very rarely beering, and the storm in the light of the summer was very rarely without it. The storm was really falling, and he stood there for a moment with his hand to open the barrier. He was so far apart, that he could not have looked at him at all then; for, it was already dark when he looked at this figure, and it looked at IV. Though he had no hope of saying it, he could have looked at him, and then frowned at another figure, whose surface furnished a kind of dark street before him, for a few jewels.
He looked at it, and glanced at it. The Spy and prison-keeper looked at it, and the Spy showed a broken-hearted look. They are all in animosity and submission. They are in the habitually consently bad. I know what you have to do with my business.
Whether I wait for you under the obligation to do so is the assumption yours. It is little to keep them in their places, to keep them in their times too much, is it not? No, Jerry. It is to keep them in their places, to cost and cost as the like. So much would you cost and change to-do exactly? That is to say, without deigning to say anything that is not at all, and no harm is to be expected of, will you not?
It will cost nothing to save you, if it wos so, refuse. But it is always in the nature of things, and it is the nature of things. What is it? What would you have to say to me at all as, or to that degree? The Judge, whose eyes had gone in the general direction, leaned back in his seat, and stood ready.
Attorney-General then, following his leader's guidance, examined his manner with great obsequiousness and closeness, and passing on to the bench and tools, and passing on to Mr. After looking at. We use optional third-party analytics cookies to understand how you use GitHub. You can always update your selection by clicking Cookie Preferences at the bottom of the page. For more information, see our Privacy Statement.We will dive into some real examples of deep learning by using open source machine translation model using PyTorch.
Through this tutorial, you will learn how to use open source translation tools. This tutorial assumes that you have prior knowledge of Python programming and Neural Machine Translation. To test if you have your GPU set and available, run these two lines of code below.
The screenshot below shows the difference in output if GPU is not available or selected from the runtime option. For example! Since, we will be training some textual data, and we need to save our data model for testing purposes. We cannot completely rely on Colab for data storage.
So, it is important to connect your session to Google Drive as an external storage. Running the code below, will help you connect to Google Drive. You will be asked to authorize through your Google account.
For further confirmation to check if you are connected to Google Drive, you can simply run the! You can upload programs necessary to run directly to the drive. Now, use the command! Then, use the command! Even though it is easy to find some existing compiled datasets specifically for Machine Translationwe will take a detour and generate some fake data by ourselves using python library Faker.
Running those few lines of code, should give you the following output, and the output will be different every time you run the code because of the random function used in the library. If you carefully look at the code, all we are doing is generating first name and last name based on the gender.
And we choose the gender randomly between male and female. We can take a much closer look to the code and let us finish our name generator code and write the output to a file.
Diving deeper into the code, I have set the localization for name generation to English-Great Britain on line 5. You can choose a different localization available and provided by faker library. And then we register our names provider class. Below command tells you how to run the code. Here in the below image you can see, I tried to run the program without any parameters.
The idea behind repeated user will be explained down the line when we start Neural Machine Translation. What our Names Provider does now is, takes two parameters 1 Unique and 2 Repeated. If you open the file, you should be able to find some repeated names. Alright, now we have a set of names which will be used as Ground Truth.
Let us introduce some random errors in the names, so we can understand if our machine translation model would be able to identify these mistakes and correct it for us once we have a fully trained model.
The above code will generate percentage amount of random unique sample in range 0 to total number of names in the file. Now, we can randomly choose names and add errors to those names, so the data corruption is not sequential. Let us break down the replacement function to understand it clearly. Since we know how to create some random fake data and also induce some error.Did you know you can park for free in the Pay Lot just by carpooling.
Pick up a punch card at the booth. Receive a punch for every passenger. One free entry with 10 punches. Did you know all students can ride the DTA for free with a UCard. Jump off and on right outside the Kirby Plaza doors.
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The only treatment for the condition is a strict gluten-free diet for life. Here are Coeliac UK's top 10 tips for everyday eating. All packaged food in the UK and the EU is covered by a law on allergen labelling, meaning you can tell whether or not a product is suitable for a gluten-free diet by reading the ingredients list.
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