Create README.md

README.md

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+This is the subset of the Reuters-21578 benchmark, contains only the documents with a single category and only the categories that have at least 1 document in both the training and testing sets, following the filtering steps by Debole and Sebastiani, 2005.
+
+I used dataset provided with NLTK Python library.
+
+There are [R52 datasets after preprocessing](https://ana.cachopo.org/datasets-for-single-label-text-categorization), provided by Ana Cardoso-Cachopo, but I couldn't find the raw R52 dataset without pre-processing.
+
+I tried my best to follow the given directions, but **there are inconsistencies with the existing dataset online**. The total number of documents in other pre-processed R52 dataset is 9,100, whereas mine is 9,130. I'm not sure where this inconsistency come from. Maybe NLTK version of Reuters-21578 has some duplicated documents over different categories. (c.f. Debole and Sebastiani mentioned that their dataset consists of 9,052 documents) So, **please use with caution**.
+
+There are 52 classes and 9,130 documents.
+
+```
+class            train  test
+acq               1596   696
+alum                31    19
+bop                 22     9
+carcass              6     5
+cocoa               46    15
+coffee              90    22
+copper              31    13
+cotton              15     9
+cpi                 54    17
+cpu                  3     1
+crude              253   121
+dlr                  3     3
+earn              2840  1083
+fuel                 4     7
+gas                 10     8
+gnp                 59    15
+gold                70    20
+grain               41    10
+heat                 6     4
+housing             15     2
+income               7     4
+instal-debt          5     1
+interest           191    81
+ipi                 34    11
+iron-steel          26    12
+jet                  2     1
+jobs                37    12
+lead                 4     4
+lei                 11     3
+livestock           16     6
+lumber              10     4
+meal-feed           10     1
+money-fx           222    87
+money-supply       123    28
+nat-gas             24    12
+nickel               3     1
+orange              13     9
+pet-chem            13     6
+platinum             1     2
+potato               2     3
+reserves            37    12
+retail              19     1
+rubber              31     9
+ship               108    36
+strategic-metal      9     6
+sugar               97    25
+tea                  2     3
+tin                 17    10
+trade              250    76
+veg-oil             19    11
+wpi                 14     9
+zinc                 8     5
+TOTAL             6560  2570
+```
+
+I do not have any copyright of this dataset.
+
+If you're using Pandas, you can load the file by
+
+```
+pandas.read_csv('r52-raw.txt', header=None, sep='\t')
+```