add requirements.txt and update readme

README.md

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+# Classifying Evolutionary Forces in Languages Change
+
+A fundamental problem in research into language and cultural change is the difficulty of
+distinguishing processes of stochastic drift (also known as neutral evolution) from
+processes that are subject to certain selection pressures. In this article, we describe a
+new technique based on Deep Neural Networks, in which we reformulate the detection of
+evolutionary forces in cultural change as a binary classification task. Using Residual
+Networks for time series trained on artificially generated samples of cultural change, we
+demonstrate that this technique is able to efficiently, accurately and consistently learn
+which aspects of the time series are distinctive for drift and selection. We compare the
+model with a recently proposed statistical test, the Frequency Increment Test, and show
+that the neural time series classification system provides a possible solution to some of
+the key problems of this test.
+
+## Data
+
+Code to reconstruct the past-tense data set can be obtained from
+https://github.com/mnewberry/ldrift. To run the past-tense analysis in
+`notebooks/past-tense.ipynb`, save the frequency list under `data/coha-past-tense.txt`. 
+
+## Requirements
+All code is implemented in Python 3.7. A detailed list of the requirements to run the code
+can be found in the `requirements.txt` file.
+
+## Training
+
+To train your own models, run `src/train.py` and follow the instructions therein. 
+
+---
+<a rel="license" href="http://creativecommons.org/licenses/by/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.
+

requirements.txt

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+numpy==1.18.1
+pandas==0.25.3
+pytorch==1.4.0
+tqdm==4.42.1
+matplotlib==3.1.2
+scikit-learn==0.22.1
+arviz==0.6.1
+scipy==1.4.1
+numba==0.47.0
+seaborn==0.10.0
+pystan==2.19.1.1
+termcolor==1.1.0