Character-level RNN for named entity recognition in plain text.
This is a special ChemPred publication branch of the sciNER package. ChemPred
is a deep CNN-RNN architecture for chemical NER trained on the CHEMDNER corpus
(publication pending). To install the package clone ше on your local machine
and run pip install --no-cache-dir .. The installer will get all dependencies,
but TensorFlow - you will have to install it yourself. If you plan to use a
CUDA-enabled GPU for training/inference, you should run
pip install --no-cache-dir tensorflow-gpu==1.3.0
otherwise run
pip install --no-cache-dir tensorflow==1.3.0
We recommend installing the package and all the dependencies in a separate
Python virtual environment (virtualenv or conda).
You can obtain the trained model from the publication from our
OneDrive.
We've included a basic CLI tool for the model to run inference: chempred-example.py,
which will be installed with the package. The tool requires a plain text file with
one sentence per line. We recommend using GeniaSS to break your text into separate
sentences, because that's what the model is used to.
We've added a Jupyter notebook chempred-training-example.ipynb to guide you through
the steps needed to train and validate the model.
You might be also interested in bench.ipynb and bench.tgz to see our benchmark pipeline.
Feel free to open issues here whenever problems occur.