Seq2seq text generation github

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Text generation with a Variational Autoencoder

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seq2seq text generation github

Go to file T Go to line L Copy path. Latest commit e6db Sep 9, History. Raw Blame. At least 20 epochs are required before the generated text starts sounding coherent. It is recommended to run this script on GPU, as recurrent networks are quite computationally intensive. Prints generated text. You signed in with another tab or window. Reload to refresh your session.

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Save preferences. Example script to generate text from Nietzsche's writings. At least 20 epochs are required before the generated text. It is recommended to run this script on GPU, as recurrent. If you try this script on new data, make sure your corpus. Function invoked at end of each epoch.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. It randomly masks a sentence fragment in the encoder, and then predicts it in the decoder.

MASS can be applied on cross-lingual tasks such as neural machine translation NMTand monolingual tasks such as text summarization.

We will release our implementation for other sequence to sequence generation tasks in the future. We release MPNeta new pre-trained method for language understanding. Unsupervised Neural Machine Translation just uses monolingual data to train the models.

During MASS pre-training, the source and target languages are pre-trained in one model, with the corresponding langauge embeddings to differentiate the langauges. During MASS fine-tuning, back-translation is used to train the unsupervised models. We provide pre-trained and fine-tuned models:.

The depencies are as follows:. During the pre-training prcess, even without any back-translation, you can observe the model can achieve some intial BLEU scores:. To use multiple GPUS across many nodesuse Slurm to request multi-node job and launch the above command. After pre-training, we use back-translation to fine-tune the pre-trained model on unsupervised machine translation:. After download the mass pre-trained model from the above link. And use the following command to fine tune:. We also implement MASS on fairseqin order to support the pre-training and fine-tuning for large scale supervised tasks, such as neural machine translation, text summarization.

Unsupervised pre-training usually works better in zero-resource or low-resource downstream tasks. However, in large scale supervised NMT, there are plenty of bilingual data, which brings challenges for conventional unsupervised pre-training.

Therefore, we design new pre-training loss to support large scale supervised NMT. The sentence X is masked and feed into the encoder, and the decoder predicts the whole sentence Y. Some discret tokens in the decoder input are also masked, to encourage the decoder to extract more informaiton from the encoder side.

seq2seq text generation github

During pre-training, we combine the orignal MASS pre-training loss and the new supervised pre-training loss together. During fine-tuning, we directly use supervised sentence pairs to fine-tune the pre-trained model. Except for NMT, this pre-trainig paradigm can be also applied on other superviseed sequence to sequence tasks. We first prepare the monolingual and bilingual sentences for Chinese and English respectively.

The data directory looks like:. The files under mono are monolingual data, while under para are bilingual data. The dictionary for different language can be different. Running the following command can generate the binarized data:. We also provide a pre-training script which is used for our released model. We also provide a fine-tuning script which is used for our pre-trained model.

After the fine-tuning stage, you can generate translation results by using the below script :. MASS for text summarization is also implemented on fairseq. The code is under MASS-summarization.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. Abstractive text summarization, on the other hand, generates summaries by compressing the information in the input text in a lossy manner such that the main ideas are preserved. The advantage of abstractive text summarization is that it can use words that are not in the text and reword the information to make the summarizes more readable. To improve the quality of the generated summaries, a Bahdanau attention mechanism, a pointer-generator network and a beam-search inference decoder are applied to the model.

You will also need to have software installed to run and execute a Jupyter Notebook. If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included.

Make sure that you select the Python 3. The dataset contains the first sentence of articles as the input text and the headlines as the ground-truth summaries. We use optional third-party analytics cookies to understand how you use GitHub.

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Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 6 commits. Failed to load latest commit information. View code. Install This project requires Python 3.

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Seq2seq Text Generation Github

Accept Reject. Essential cookies We use essential cookies to perform essential website functions, e. Analytics cookies We use analytics cookies to understand how you use our websites so we can make them better, e. Save preferences.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. 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 use essential cookies to perform essential website functions, e.

We use analytics cookies to understand how you use our websites so we can make them better, e. Skip to content. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats commits. Failed to load latest commit information. View code. About No description, website, or topics provided.

Releases No releases published. Packages 0 No packages published.Universal seq2seq model for question generation, text summarization, machine translation etc.

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. Universal sequence-to-sequence model with attention and beam search for inference decoding.

Neural machine translation with attention

Should work for text summarization, neural machine translation, question generation etc. In my case I've used it for question generation - I've trained the model on reversed SQuAD dataset with paragraphs as my input and questions as my targets.

The SQuAD parsing script is also included in the depository. The data and checkpoints are not included as they simply weight too much, but feel free to experiment around with the model and let me know your results!

I believe every function is nicely commented, so I won't go too much into details here as the code speaks for itself. For the data preprocessing I'm using spaCy as I've came to rely heavily on it when it comes to any NLP tasks - it's simply brilliant.

In this case, spaCy is used to remove stopwords and punctuation from the dataset as well as exchange any entities in the text into their corresponding labels, such as LOC, PERSON, DATE etc, so the model learns dependencies between paragraph and question and does not overfit.

The model doesn't produce any staggering results for the question generation task with the dataset I've used, however, I can imaging with more examples the output would make more sense.

He had the theses checked for heresy and in December forwarded them to Rome. He needed the revenue from the indulgences to pay off a papal dispensation for his tenure of more than one bishopric. As Luther later noted, "the pope had a finger in the pie as well, because one half was to go to the building of St Peter's Church in Rome". 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 use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e. Skip to content. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 9 commits.Welcome back! A generative model for text in Deep Learning is a neural network based model capable of generating text conditioned on a certain input. To do so it relies on a language model, that is nothing else than a probability distribution over a sequence of words. Given different word sequences a language model can assign a probability to each sequence and rank their relative likelihoods and thus, in generative models, they allow to generate text sequence with the highest likelihood.

One interesting aspect of language models is that they can exploit unsupervised learning from unlabelled data because the labels are intrinsic in the language structure itself. Normally, the labels to train the network are the following word or character or also the entire sentence sequence when working with autoencoders.

There are several types of language models in deep learning; the most common are based on Recurrent Neural Network, Sequence to Sequence Seq2seq models and Attentional Recurrent Neural Networks.

RNN模型与NLP应用(6/9):Text Generation (自动文本生成)

The simplest form of language model is a recurrent neural network trained to predict the next token character or word given the previous tokens link example. Sequence to sequence models have a more complex architecture made of two recurrent neural networks one encoder network and one decoder network.

Attention based models are more recent architectures that overcome the limitation of a fixed size representation of seq2seq models by feeding to the decoder network a concatenation of the encoder network output sequence weighted by the socalled attention mechanism.

There are also hybrid architectures that mix convolutional neural networks with recurrent network in seq2seq models like in this paper. The model that we are going to implement is a variational autoencoder based on a seq2seq architecture with two recurrent neural networks encoder and decoder and a module that performs the variational approximation.

seq2seq text generation github

It encodes data to latent random variables, and then decodes the latent variables to reconstruct the data. Its main applications are in the image domain but lately many interesting papers with text applications have been published, like the one we are trying to replicate. The VAE solves this problem since it explicitly defines a probability distribution on the latent code.

In fact, it learns the latent representations of the inputs not as single points but as soft ellipsoidal regions in the latent space, forcing the latent representations to fill the latent space rather than memorizing the inputs as punctual, isolated latent representations. To obtain this, the model is trained by maximizing a variational lower bound on the data log-likelihood under the generative model.

The KL regularization in the variational lower bound, enables every latent code from the prior to decode into a plausible sentence. Without the KL regularization term, VAEs degenerate to deterministic autoencoders and become inapplicable for the generic generation. The model that we are going to implement is based on a Seq2Seq architecture with the addition of a variational inference module.

All the network is trained simultaneously via stochastic gradient descent. Here you can find the Jupyter notebook and the code on Github. Initially we will set the main directories and some variables regarding the characteristics of our texts. We set the maximum sequence length to 15, the maximun number of words in our vocabulary to and we will use dimensional embeddings. Finally we load our texts from a csv. The text file is the train file of the Quora Kaggle challenge containing around sentences.

In order to reduce the memory requirements we will gradually read our sentences from the csv through Pandas as we feed them to the model. We will use pretrained Glove word embeddings as embeddings for our network. We create a matrix with one embedding for every word in our vocabulary and then we will pass this matrix as weights to the keras embedding layer of our model.

For validation data we pass the same array twice since input and labels of this model are the same. Now we build an encoder model model that takes a sentence and projects it on the latent space and a decoder model that goes from the latent space back to the text representation.This notebook trains a sequence to sequence seq2seq model for Spanish to English translation. This is an advanced example that assumes some knowledge of sequence to sequence models. The translation quality is reasonable for a toy example, but the generated attention plot is perhaps more interesting.

This shows which parts of the input sentence has the model's attention while translating:. There are a variety of languages available, but we'll use the English-Spanish dataset.

For convenience, we've hosted a copy of this dataset on Google Cloud, but you can also download your own copy. After downloading the dataset, here are the steps we'll take to prepare the data:. To train faster, we can limit the size of the dataset to 30, sentences of course, translation quality degrades with less data :.

Implement an encoder-decoder model with attention which you can read about in the TensorFlow Neural Machine Translation seq2seq tutorial. This example uses a more recent set of APIs. This notebook implements the attention equations from the seq2seq tutorial. The following diagram shows that each input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence.

The below picture and formulas are an example of attention mechanism from Luong's paper. This tutorial uses Bahdanau attention for the encoder. Let's decide on notation before writing the simplified form:. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.

For details, see the Google Developers Site Policies. Install Learn Introduction. TensorFlow Lite for mobile and embedded devices. TensorFlow Extended for end-to-end ML components. TensorFlow r2.


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