Slides and code for the intro to NLP class at the Data Science Retreat @ Berlin, 28 Oct 2019
Clone this repo with
git clone --recursive https://github.com/davidalbertonogueira/NLP-tutorials
git submodule update --depth 10 --recursive --init
Besides python >= 3.7 and pip, the required libraries are listed in requirements.txt.
Code is cross-platform. Tested in Windows and Ubuntu 18.04.
| Linux | Windows |
|---|---|
| sudo apt-get install virtualenv / pip install virtualenv | pip install virtualenv |
| virtualenv --python /usr/bin/python3.7 venv | virtualenv venv |
| source venv/bin/activate | venv\Scripts\activate.bat |
| pip install -r requirements.txt | pip install -r requirements.txt |
| pip install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp37-cp37m-linux_x86_64.whl (1)* | pip install http://download.pytorch.org/whl/cpu/torch-0.4.1-cp37-cp37m-win_amd64.whl (1)* |
| pip install torchvision | pip install torchvision |
| pip install torchtext==0.2.3 | pip install torchtext==0.2.3 |
| python -m spacy download en_core_web_sm | python -m spacy download en_core_web_sm |
(1)* replace "cpu" in link if you plan to use GPU: "cu80" for CUDA 8, "cu90" for CUDA 9.0, "cu92" for CUDA 9.2, ...
If you require a newer version, please visit http://pytorch.org/ and follow their instructions to install the relevant pytorch binary.
Text Classification with BoW + TF-IDF + Naive bayes and SVM based on http://github.com/Gunjitbedi/Text-Classification
Text Classification with word embeddings + neural network
Sentiment Analysis with task-specific word embeddings + Pytorch neural network
The implemented model is a Deep Bidirectional LSTM model with Attention, based on the work of Baziotis et al., 2017: DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis.
Recomendation using Colaborative filtering
Transition-based NER system (in C++ Dynet) (Pytorch version)
Paper: https://arxiv.org/pdf/1603.01360.pdf
Transformers: Attention is all you need copied from http://nlp.seas.harvard.edu/2018/04/03/attention.html