Community-driven code for the book Natural Language Processing in Action.
A community-developed book about building socially responsible NLP pipelines that give back to the communities they interact with.
You'll need a bash shell on your machine. Git has installers that include bash shell for all three major OSes.
Once you have Git installed, launch a bash terminal.
It will usually be found among your other applications with the name
If you're installing Anaconda3 using a GUI, be sure to check the box that updates your PATH variable. Also, at the end, the Anaconda3 installer will ask if you want to install VSCode. Microsoft's VSCode is supposed to be an OK editor for Python so feel free to use it.
Step 2. Install an Editor
You can skip this step if you are happy using
jupyter notebook or
VSCode or the editor built into Anaconda3.
I like Sublime Text. It's a lot cleaner more mature. Plus it has more plugins written by individual developers like you.
Step 3. Install Git and Bash
- Linux -- already installed
- MacOSX -- already installed
If you're on Linux or Mac OS, you're good to go. Just figure out how to launch a terminal and make sure you can run
jupyter notebook in it. This is where you'll play around with your own NLP pipeline.
On Windows you have a bit more work to do. Supposedly Windows 10 will let you install Ubuntu with a terminal and bash. But the terminal and shell that comes with
git is probably a safer bet. It's mained by a broader open source community.
Step 4. Clone this repository
git clone https://github.com/totalgood/nlpia.git
Step 5. Install
You have two alternative package managers you can use to install
In most cases,
conda will be able to install python packages faster and more reliably than pip. Without
conda Some packages, such as
python-levenshtein, require you to compile a C library during installation. Windows doesn't have an installer that will "just work."
Use conda (part of the Anaconda package that you installed in Step 1 above) to create an environment called
cd nlpia # make sure you're in the nlpia directory that contains `setup.py` conda env create -n nlpiaenv -f conda/environment.yml conda install pip # to get the latest version of pip source activate nlpiaenv pip install -e .
Whenever you want to be able to import or run any
nlpia modules, you'll need to activate this conda environment first:
$ source activate nlpiaenv
On Windows CMD prompt (Anaconda Prompt in Applications) there is no source command so:
C:\ activate nlpiaenv
Now you can finally make sure you can import nlpia with:
python -c "print(import nlpia)"
Skip to Step 6 ("Have fun!") if you have successfully created and activated an environment containing the
nlpia package and its dependencies.
You can try this first, if you're feeling lucky:
cd nlpia pip install --upgrade pip pip install -e .
Or if you don't think you'll be editing any of the source code for nlpia your can just:
pip install nlpia
Linux-based OSes like Ubuntu and OSX come with C++ compilers built-in, so you may be able to install the dependencies using pip instead of
But if you're on Windows and you want to install packages, like
python-levenshtein that need compiled C++ libraries, you'll need a compiler.
Fortunately Microsoft still lets you download a compiler for free, just make sure you follow the links to the Visual Studio "Build Tools" and not the entire Visual Studio package.
Once you have a compiler on your OS you can install
nlpia using pip:
cd nlpia # make sure you're in the nlpia directory that contains `setup.py` pip install --upgrade pip mkvirtualenv nlpiaenv source nlpiaenv/bin/activate pip install -r requirements-test.txt pip install -e . pip install -r requirements-deep.txt
The chatbots(including TTS and STT audio drivers) that come with
nlpia may not be compatible with Windows due to problems install
If you are on a Linux or Darwin(Mac OSX) system or want to try to help us debug the pycrypto problem feel free to install the chatbot requirements:
# pip install -r requirements-chat.txt # pip install -r requirements-voice.txt
6. Have Fun!
Check out the code examples from the book in
nlpia/nlpia/book/examples to get ideas:
cd nlpia/book/examples ls
Help other NLP practicioners by contributing your code and knowledge. Here are some ideas for a few features others might find handy.
1. Build your image (This process might take few minutes for download jupyter docker image)
docker build -t nlpia .
2. Run your image
docker run -p 8888:8888 nlpia
- Copy the
tokenobtained from the run log
- Open Browser and use the link
3. Play around
If you want to keep your notebook file or share a folder with the running container then use the command:
docker run -p 8888:8888 -v ~:/home/jovyan/work nlpia
Open new notebook and test your code, and make sure save it inside
Feature 1: Glossary Compiler
Skeleton code and APIs that could be added to the https://github.com/totalgood/nlpia/blob/master/src/nlpia/transcoders.py:`transcoders.py` module.
def find_acronym(text): """Find parenthetical noun phrases in a sentence and return the acronym/abbreviation/term as a pair of strings. >>> find_acronym('Support Vector Machine (SVM) are a great tool.') ('SVM', 'Support Vector Machine') """ return (abbreviation, noun_phrase)
def glossary_from_dict(dict, format='asciidoc'): """ Given a dict of word/acronym: definition compose a Glossary string in ASCIIDOC format """ return text
def glossary_from_file(path, format='asciidoc'): """ Given an asciidoc file path compose a Glossary string in ASCIIDOC format """ return text def glossary_from_dir(path, format='asciidoc'): """ Given an path to a directory of asciidoc files compose a Glossary string in ASCIIDOC format """ return text
Feature 2: Semantic Search
Use a parser to extract only natural language sentences and headings/titles from a list of lines/sentences from an asciidoc book like "Natural Language Processing in Action". Use a sentence segmenter in https://github.com/totalgood/nlpia/blob/master/src/nlpia/transcoders.py:[nlpia.transcoders] to split a book, like NLPIA, into a seequence of sentences.
Feature 3: Semantic Spectrograms
A sequence of word vectors or topic vectors forms a 2D array or matrix which can be displayed as an image. I used
nlpia.loaders.get_data('word2vec')) to embed the words in the last four paragraphs of Chapter 1 in NLPIA and it produced a spectrogram that was a lot noisier than I expected. Nonetheless stripes and blotches of meaning are clearly visible.
First, the imports:
>>> from nlpia.loaders import get_data >>> from nltk.tokenize import casual_tokenize >>> from matplotlib import pyplot as plt >>> import seaborn
First get the raw text and tokenize it:
>>> lines = get_data('ch1_conclusion') >>> txt = "\n".join(lines) >>> tokens = casual_tokenize(txt) >>> tokens[-10:] ['you', 'accomplish', 'your', 'goals', 'in', 'business', 'and', 'in', 'life', '.']
Then you'll have to download a word vector model like word2vec:
>>> wv = get_data('w2v') # this could take several minutes >>> wordvectors = np.array([wv[tok] for tok in tokens if tok in wv]) >>> wordvectors.shape (307, 300)
Now you can display your 307x300 spectrogram or "wordogram":
>>> plt.imshow(wordvectors) >>> plt.show()
Can you think of some image processing or deep learning algorithms you could run on images of natural language text?
Once you've mastered word vectors you can play around with Google's Universal Sentence Encoder and create spectrograms of entire books.
Feature 5: Build your own Sequence-to-Sequence translator
If you have pairs of statements or words in two languages, you can build a sequence-to-sequence translator. You could even design your own language like you did in gradeschool with piglatin or build yourself a L337 translator.
There are a lot more project ideas mentioned in the "Resources" section at the end of NLPIA. Here's an early draft of that resource list.