Update README to use Castor-models and Instructions for Internal Users (

#113)

* Update instructions to use Castor-models
* Consolidate requirements.txt
* Refine README with convenience scripts
* Update internal instructions
* MP-CNN working dir minor edit

README.md

@@ -17,39 +17,66 @@ For paraphrase detection, question answering, etc.
 
 + [SM-CNN](./sm_cnn/): Siamese CNN for ranking texts [(Severyn and Moschitti, SIGIR 2015)](https://dl.acm.org/citation.cfm?id=2767738)
 + [MP-CNN](./mp_cnn/): Multi-Perspective CNN [(He et al., EMNLP 2015)](http://anthology.aclweb.org/D/D15/D15-1181.pdf)
-+ [NCE](./nce/): Noise-Contrastive Estimation for answer selection applied on SM-CNN and MP-CNN
++ [NCE](./nce/): Noise-Contrastive Estimation for answer selection applied on SM-CNN and MP-CNN [(Rao et al., CIKM 2016)](https://dl.acm.org/citation.cfm?id=2983872)
 + [IDF Baseline](./idf_baseline/): IDF overlap between question and candidate answers
 
+Each model directory has a `README.md` with further details.
+
 ## Setting up PyTorch
 
-Copy and run the command at https://pytorch.org/ for your environment. PyTorch recommends the Anaconda environment, which we use in our lab.
+**If you are an internal Castor contributor and is planning to use the Data System Group's GPU machines in the lab,
+please follow the instructions [here](./docs/internal-instructions.md) instead.**
+
+Copy and run the command at [https://pytorch.org/](https://pytorch.org/) for your environment.
+PyTorch recommends the Anaconda environment, which we use in our lab. We are currently targeting PyTorch 0.4 for our codebase.
 
 The typical installation command is
 
 ```bash
 conda install pytorch torchvision -c pytorch
 ```
 
+Other Python packages we use can be installed via pip:
+
+```bash
+pip install -r requirements.txt
+```
+
+Please also run the following inside the `utils` directory to build the `trec_eval` tool for evaluating certain datasets.
+
+```bash
+./get_trec_eval.sh
+```
+
 ## Data and Pre-Trained Models
 
+**If you are an internal Castor contributor and is planning to use the Data System Group's GPU machines in the lab,
+please follow the instructions [here](./docs/internal-instructions.md) instead.**
+
 Data associated for use with this repository can be found at: https://git.uwaterloo.ca/jimmylin/Castor-data.git.
 
-Pre-trained models can be found at: https://github.com/castorini/models.git.
+Pre-trained models can be found at: https://git.uwaterloo.ca/jimmylin/Castor-models.
 
 Your directory structure should look like
 ```
 .
 ├── Castor
 ├── Castor-data
-└── models
+└── Castor-models
 ```
 
 For example (if you use HTTPS instead of SSH):
 
 ```bash
 git clone https://github.com/castorini/Castor.git
 git clone https://git.uwaterloo.ca/jimmylin/Castor-data.git
-git clone https://github.com/castorini/models.git
+git clone https://git.uwaterloo.ca/jimmylin/Castor-models.git
 ```
 
-Sourcing and pre-processing of input data for each model is described in the respective ```model/README.md```'s.
+After cloning the Castor-data repo, you need to unzip embeddings and run data pre-processing scripts. You can choose
+to follow instructions under each dataset / embedding directory separately, or just run the following script in Castor-data
+to do all of the steps for you:
+
+```bash
+./setup.sh
+```

docs/internal-instructions.md

@@ -0,0 +1,49 @@
+# Instructions for DSG Castor Contributors
+
+Please follow these instructions if you are a graduate student or undergrad research assistant working with the group
+in the Data Systems Lab and want to run Castor on the lab desktop GPU machine (dragon).
+
+If you have trouble / questions with instructions on this page, ping @tuzhucheng on Slack.
+
+## PyTorch Environment
+
+We already have a multi-user Conda environment with PyTorch and all other dependencies installed, so you do not need to
+install anything yourself. However, you can create [Conda environments](https://conda.io/docs/user-guide/tasks/manage-environments.html)
+if you need to experiment with different library versions etc.
+
+The multi-user Conda environment is located at `/anaconda3/`.
+To use this multi-user environment, just add the following to your `.bashrc` or configuration file for your favourite shell.
+
+```bash
+export PATH="/anaconda3/bin:$PATH"
+export LIBRARY_PATH="/usr/lib/nvidia-375"
+```
+
+Please also ensure `/usr/local/cuda-8.0/lib64` is in the `LD_LIBRARY_PATH` environment variable **if it is not already**.
+If not, you should add it in the `.bashrc` similar to above.
+
+Please re-login or re-source your shell configuration after `.bashrc` is updated for the updated environment variables
+to take effect.
+
+## Data and Pre-Trained Models
+
+We use shared cloned versions of the Castor-data and Castor-models repositories.
+Instead of making your own cloned copies, you can just create symbolic links to the shared version instead
+in your own working directory to save disk space. Assuming you want to put `Castor`, `Castor-data`, and `Castor-models`
+under a directory called `castorini` and you are currently in the `castorini` directory, you can enter these commands:
+
+```bash
+ln -s /Castor-data Castor-data
+ln -s /Castor-models Castor-models
+```
+
+So after you clone Castor, you have a directory structure under `castorini` that looks like this:
+
+```
+.
+├── Castor
+├── Castor-data
+└── Castor-models
+```
+
+where `Castor-data` and `Castor-models` are actually symbolic links to `/Castor-data` and `/Castor-models`.

mp_cnn/README.md

@@ -5,6 +5,7 @@ This is a PyTorch implementation of the following paper
 * Hua He, Kevin Gimpel, and Jimmy Lin. [Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks](http://aclweb.org/anthology/D/D15/D15-1181.pdf). *Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015)*, pages 1576-1586.
 
 Please ensure you have followed instructions in the main [README](../README.md) doc before running any further commands in this doc.
+The commands in this doc assume you are under the root directory of the Castor repo.
 
 ## Pre-Trained Models
 

requirements.txt

@@ -1,7 +1,9 @@
+Flask==0.12.1
 gensim==1.0.1
-numpy==1.12.1
+nltk==3.2.5
+numpy==1.14.0
 pandas==0.19.2
-Flask==0.12.1
-nltk==3.2.2
 pyjnius==1.1.1
--e git+https://github.com/castorini/Castor.git#egg=sm-cnn-1.0.0
+scikit-learn==0.19.1
+scipy==1.0.0
+torchtext==0.2.3