A Modern Toolkit for Scientific Document Processing from WING-NUS. You can find our technical report here: https://arxiv.org/abs/2004.03807.
Note The previous demo was available at bit.ly/sciwing-demo. Due to unavoidable circumstances, it has been moved to rebrand.ly/sciwing-demo.
SciWING is a modern framework from WING-NUS to facilitate Scientific Document Processing. It is built on PyTorch and believes in modularity from ground up and easy to use interface. SciWING includes many pre-trained models for fundamental tasks in Scientific Document Processing for practitioners. It has the following advantages:
Modularity - The framework embraces modularity from ground-up. SciWING helps in creating new models by combining multiple re-usable modules. You can combine different modules and experiment with new approaches in an easy manner
Pre-trained Models - SciWING has many pre-trained models for fundamental tasks like Logical Section Classifier for scientific documents, Citation string Parsing (Take a look at some of the other project related to station parsing Parscit, Neural Parscit. Easy access to pre-trained models are made available through web APIs.
Run from Config File- SciWING enables you to declare datasets, models and experiment hyper-params in a TOML file. The models declared in a TOML file have a one-one correspondence with their respective class declaration in a python file. SciWING parses the model to a Directed Acyclic Graph and instantiates the model using the DAG's topological ordering.
Extensible - SciWING enables easy addition of new datasets and provides command line tools for it. It enables addition of custom modules which are PyTorch modules.
You can install SciWING from pip. We recommend using a virtual environment to install the package.
pip install sciwing # install spacy language pack python -m spacy download en_core_web_sm
These are some of the tasks included in SciWING and their performance metrics
|Task||Dataset||SciWING model||SciWING||Previous Best|
|Logical Structure Recovery||SectLabel||BiLSTM + Elmo Embeddings||73.2 (Macro F-score)||-|
|Header Normalisation||SectLabel||Bag of Words Elmo||93.52 (Macro F-Score)||-|
|Citation String Parsing||Neural Parscit||Bi-LSTM-CRF + GloVe + Elmo + Char-LSTM||88.44 (Macro F-Score)||90.45 Prasad et al(not comparable)|
|Citation Intent Classification||SciCite||Bi-LSTM + Elmo||82.16 (Fscore)||82.6 Cohan et al (without multi-task learning)|
|I2b2 NER||I2B2||Bi-LSTM + Elmo||85.83 (Macro FScore)||86.23 Boukkouri et al|
|BC5CDR - NER (Upcoming)||-||-||-||-|
Using Citation String Parsing
from sciwing.models.neural_parscit import NeuralParscit # instantiate an object neural_parscit = NeuralParscit() # predict on a citation neural_parscit.predict_for_text("Calzolari, N. (1982) Towards the organization of lexical definitions on a database structure. In E. Hajicova (Ed.), COLING '82 Abstracts, Charles University, Prague, pp.61-64.") # if you have a file of citations with one citation per line neural_parscit.predict_for_file("/path/to/filename")
Here is the output of the above example:
Using Citation Intent Classification
from sciwing.models.citation_intent_clf import CitationIntentClassification # instantiate an object citation_intent_clf = CitationIntentClassification() # predict the intention of the citation citation_intent_clf.predict_for_text("Abu-Jbara et al. (2013) relied on lexical,structural, and syntactic features and a linear SVMfor classification.")
Running API services
The APIs are built using Fast API. We have APIs for citation string parsing, citation intent classification and many other models. There are more APIs on the way. To run the APIs navigate into the
api folder of this repository and run
uvicorn api:app --reload
Running the Demos
The demos are built using Streamlit. The Demos make use of the APIs. Please make sure that the APIs are running before the demos can be started. Navigate to the app folder and run the demo using streamlit (Installed along with the package). For example, this command runs all the demos.
Note: The demos download the models and the embeddings if already not downloaded and running the first time on your local machine might take time and memory. We have tested this on a 16GB MacBook Pro and works well. All the demos run on CPU for now and does not make use of any GPU, even when present.
streamlit run all_apps.py
Thank you for your interest in contributing. You can directly email the author at (email omitted for submission purposes). We will be happy to help.
If you want to get involved in the development we recommend that you install SciWING on a local machine using the instructions below. All our classes and methods are documented and hope you can find your way around it.
Instructions to install SciWING locally
SciWING requires Python 3.7, We recommend that you install
Instructions to install pyenv are available here. If you have problems installing python 3.7 on your machine, make sure to check out their common build problems site here and install all dependencies.
Clone from git
git clone https://github.com/abhinavkashyap/sciwing.git
Install all the requirements
pip install -r requirements.txt
Download spacy models
python -m spacy download en
Install the package locally
pip install -e .
Create directories where sciwing stores embeddings and experiment results
sciwing develop makedirs
sciwing develop download
This will take some time to download all the data and embeddings required for development
☕. Come back later
pytestfor testing. You can use the following command to run tests
pytest tests -n auto --dist=loadfile
The test suite is huge and again, it will take some time to run. We will put efforts to reduce the test time in the next iterations.