A Deep Dive into NLP with PyTorch

Learn how to use PyTorch to solve some common NLP problems with deep learning. View these notebooks on nbviewer.

1_BoW_text_classification.ipynb: Train a bag-of-words model to predict the sentiment of IMDB reviews
2_embeddings.ipynb: Play around with different pretrained word embeddings
3_rnn_text_classification.ipynb: Train an RNN to predict the sentiment of IMDB movie reviews
4_character_text_generation.ipynb: Train a character-level RNN language model to generate weight loss articles
5_seq2seq_attention_translation.ipynb: Train an RNN-based Seq2Seq model with attention to translate from English to French
6_transformer_translation.ipynb: Train a pure self-attention based transformer Seq2Seq model to translate from English to French
7_gpt2_finetuned_text_generation.ipynb: Fine-tune the pretrained (small) GPT-2 model to generate weight loss articles

Tutorials

Events	Dates	Slides
PyData London 2019	12 Jul 2019	link
PyData Cambridge 2019	15 Nov 2019	link
DS Con Belgrade 2019	18 Nov 2019	link

Setup

Make sure you have a Google account and visit Google Colab. You should see a list of notebooks pop up:

If you have trouble with that, you can also save the notebook you want to run from this repo to your local filesystem, and then upload it to Google Colab with File -> Open Notebook -> Upload.

Basic Navigation

You can run cells with <SHIFT> + <ENTER>.

Missing packages

If you find that you are missing a necessary package, you can prepend ! to a bash command. For example, to install googledrivedownloader, you would run in a cell:

!pip install googledrivedownloader

Using a GPU

To use a GPU (for free!), select from the top menu from Colab Runtime -> Change Runtime Type -> Hardware Accelerator -> GPU. Pay attention to how much memory the GPU is currently using by clicking Runtime -> Manage Sessions.

Contributing