update tf>=2.0

.gitattributes

@@ -1,3 +1 @@
-data/annotations.tsv filter=lfs diff=lfs merge=lfs -text
-data/monument_300.zip filter=lfs diff=lfs merge=lfs -text
-data/monument_600.zip filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text

README.md

@@ -2,19 +2,13 @@
 
 [![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)
 
-A LSTM-based Machine Translation Approach for Question Answering over Knowledge Graphs.
+A Attention-based Machine Translation Approach for Question Answering over Knowledge Graphs.(https://www.tensorflow.org/tutorials/text/nmt_with_attention)
 
 ![What does a NSpM do?](http://www.liberai.org/img/NSpM-image.png "What does a NSpM do?")
 
 ## Code
 
-Install `git-lfs` in your machine, then fetch all files and submodules.
-
-```bash
-git lfs fetch
-git lfs checkout
-git submodule update --init
-```
+clone the repo
 
 ### Python setup
 
@@ -26,50 +20,43 @@ pip install -r requirements.txt
 
 #### Pre-generated data
 
-You can extract pre-generated data from `data/monument_300.zip` and `data/monument_600.zip` in folders having the respective names.
+You can extract pre-generated data and model checkpoints from `data/art_30.zip` in folders having the respective names.
 
 #### Manual Generation (Alternative to using pre-generated data)
 
-The template used in the paper can be found in a file such as `annotations_monument.tsv`. `data/monument_300` will be the ID of the working dataset used throughout the tutorial. To generate the training data, launch the following command.
+The template used in the paper can be found in a file such as `Annotations_F30_art.csv`. `data/art_30` will be the ID of the working dataset used throughout the tutorial. To generate the training data, launch the following command.
 
 <!-- Made monument_300 directory in data directory due to absence of monument_300 folder in data directory  -->
 ```bash
-mkdir data/monument_300
-python generator.py --templates data/annotations_monument.csv --output data/monument_300
+mkdir data/art_30
+python generator.py --templates data/Annotations_F30_art.csv --output data/art_30
 ```
 
-Launch the command to build the vocabularies for the two languages (i.e., English and SPARQL) and split into train, dev, and test sets.
+Launch the command if you want to build dataset seprately else it will internally be called while training.
 
 ```bash
-./generate.sh data/monument_300
+python data_gen.py --input data/art_30 --output data/art_30
 ```
 
 ### The Learner module
 
 <!-- Just a simple note to go back to the initial directory.-->
-Now go back to the initial directory and launch `train.sh` to train the model. The first parameter is the prefix of the data directory and the second parameter is the number of training epochs.
+Now go back to the initial directory and launch `train.py` to train the model. Currently the epochs and batch_size is not parametrized for that you can change the epoch is train.py and batch size in data_gen.py (recommended batch size for large 64, medium 32 and small like art_30 is 16) also epochs varies with batch size for art 30 its 40.
 
 ```bash
-./train.sh data/monument_300 12000
+python train.py --input data/art_30 --output data/art_30
 ```
 
-This command will create a model directory called `data/monument_300_model`.
+This command will create a model checkpoints in `data/art_30`.
 
 ### The Interpreter module
 
-Predict the SPARQL query for a given question with a given model.
+Predict the SPARQL query for a given question it will store the detailed output in output_query.
 
 ```bash
-./ask.sh data/monument_300 "where is edward vii monument located in?"
+python translate.py --input data/art_30 --output data/art_30 --inputstr "yuncken freeman has architected in how many cities?"
 ```
 
-### Unit tests
-
-Tests can be run, but exclusively within the root directory.
-
-```bash
-py.test *.py
-```
 
 ## Use cases & integrations
 
@@ -138,4 +125,4 @@ py.test *.py
 * Follow the [project on ResearchGate](https://www.researchgate.net/project/Neural-SPARQL-Machines).
 * Follow Liber AI Research on [Twitter](https://twitter.com/theLiberAI).
 
-<p align="center"><img tooltip="Liber AI" src="http://www.liberai.org/img/Liber-AI-logo-name-200px.png" alt="Liber AI logo" border="0"></p>
+<p align="center"><img tooltip="Liber AI" src="http://www.liberai.org/img/Liber-AI-logo-name-200px.png" alt="Liber AI logo" border="0"></p>