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Reading only tutorial

In this example, we provide step-by-step instructions for running the Hume machine reading system on a set of sample documents.

In the steps below, we assume that our working folder is runtime, that this folder already exists and all work is done under this path. This is arbitrary and can be replaced by another appropriate local path.

This example assumes that the image for the Hume container has already been downloaded from Docker hub.

1. Download sample documents

As a first step, we need to identify a set of sample documents to use as input. The World Modelers demo set of documents are publicly available on Github. The demo document set includes both the raw source documents and the CDR files which contain both the text of the raw documents and some metadata. In this example we’ll use the CDRs as input to Hume.

cd runtime
git clone

The input documents will now be in the runtime/world-modelers-demo-docs/cdrs folder.

2. Configure the Ontology

For the purposes of this example, we assume the latest version of the default World Modelers ontology will be used. Normally, if using this ontology, no extra steps are needed, however, here we make its use explicit to better illustrate how a custom ontology could be used.

We will put the ontology in runtime/ontology.yml, the following command doing the download:

wget -O runtime/ontology.yml

3. Create an output directory

Create an output directory inside the working folder so that it can be accessed by both the local system and the docker instance.

mkdir runtime/output

4. Create a Docker config file

Next we use a text editor to create a configuration file in runtime/config.json that tells Hume where to look for the input documents and ontology. The contents of the file should be as follows:

  "hume.domain": "WM",
  "hume.num_of_vcpus": 4,
  "hume.tmp_dir": "/extra/output",
  "hume.manual.cdr_dir": "/extra/world-modelers-demo-docs/cdrs",
  "hume.external_ontology_path": "/extra/ontology.yml",
  "hume.use_regrounding_cache": false

In this example we assume a system with access to 4 physical CPU cores. The hume.num_of_vcpus setting may need to be adjusted to reflect your own hardware specs.

5. Run Hume on the documents

Finally, we run Hume on the input documents. Hume will write its output to the runtime/output/results/$TIMESTAMP/results folder, where $TIMESTAMP refers to a string representing the date/time of the run.

Note: In the command below, replace the $RUNTIME variable with the absolute path of your runtime folder (for example /home/wmuser/runtime).

docker run -it -v $RUNTIME:/extra /usr/local/envs/py3-jni/bin/python3 /wm_rootfs/git/Hume/src/python/dart_integration/

Reading can take a few hours. For faster results, start with an input folder that only contains a subset of CDRs.