add reuters

README.md

@@ -3,4 +3,30 @@ license: other
 license_name: topicnet
 license_link: >-
   https://github.com/machine-intelligence-laboratory/TopicNet/blob/master/LICENSE.txt
+configs:
+  - config_name: "bag-of-words"
+    default: true
+    data_files:
+      - split: train
+        path: "data/Reuters_BOW.csv.gz"
+  - config_name: "natural-order-of-words"
+    data_files:
+      - split: train
+        path: "data/Reuters_NOOW.csv.gz"
 ---
+
+
+# Reuters
+
+The Reuters Corpus contains 10,788 news documents totaling 1.3 million words. The documents have been classified into 90 topics, and grouped into two sets, called "training" and "test"; thus, the text with fileid 'test/14826' is a document drawn from the test set. This split is for training and testing algorithms that automatically detect the topic of a document, as we will see in chap-data-intensive.
+
+* Language:           English
+* Number of topics:   90
+* Number of articles: ~10.000
+* Year:               2000
+
+
+## References
+
+* NLTK datasets: https://www.nltk.org/book/ch02.html.
+* Dataset site: https://trec.nist.gov/data/reuters/reuters.html.

data/Reuters_BOW.csv.gz

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+size 5306187

data/Reuters_NOOW.csv.gz

@@ -0,0 +1,3 @@
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+oid sha256:c6df18d47ddd64b70d1b4c96b0f557a918ac4d4076fb52a42293e9df9a21e204
+size 5145624