| # cnn-text-classification |
| |
| An example of text classification using CNN |
| |
| To use you must download the MR polarity dataset and put it in the path specified in the mr-dataset-path |
| The dataset can be obtained here: [https://github.com/yoonkim/CNN_sentence](https://github.com/yoonkim/CNN_sentence). The two files `rt-polarity.neg` |
| and `rt-polarity.pos` must be put in a directory. For example, `data/mr-data/rt-polarity.neg`. |
| |
| You also must download the glove word embeddings. The suggested one to use is the smaller 50 dimension one |
| `glove.6B.50d.txt` which is contained in the download file here [https://nlp.stanford.edu/projects/glove/](https://nlp.stanford.edu/projects/glove/) |
| |
| ## Usage |
| |
| You can run through the repl with |
| `(train-convnet {:embedding-size 50 :batch-size 100 :test-size 100 :num-epoch 10 :max-examples 1000})` |
| |
| or |
| `JVM_OPTS="Xmx1g" lein run` (cpu) |
| |
| You can control the devices you run on by doing: |
| |
| `lein run :cpu 2` - This will run on 2 cpu devices |
| `lein run :gpu 1` - This will run on 1 gpu device |
| `lein run :gpu 2` - This will run on 2 gpu devices |
| |
| |
| The max-examples only loads 1000 each of the dataset to keep the time and memory down. To run all the examples, |
| change the main to be (train-convnet {:embedding-size 50 :batch-size 100 :test-size 1000 :num-epoch 10) |
| |
| and then run |
| |
| - `lein uberjar` |
| - `java -Xms1024m -Xmx2048m -jar target/cnn-text-classification-0.1.0-SNAPSHOT-standalone.jar` |
