Update README.md

README.md

@@ -1,14 +1,26 @@
+---
+license: unknown
+task_categories:
+- text-classification
+language:
+- en
+tags:
+- Reuters
+pretty_name: R52 Dataset without Pre-processing
+size_categories:
+- 1K<n<10K
+---
 This is the subset of the Reuters-21578 benchmark for (plain) text classification.
 
-It contains only the documents with a single category label and only the categories that have at least 1 document in both the training and testing sets, same as the filtering scheme by H. Guan et al., 2009. It's suitable for text classification, especially using the models with their own tokenizers such as BERT, which shows good performance on the plain text.
+It contains only the documents with a single category label and only the categories that have at least 1 document in both the training and testing sets, the same as the filtering scheme by H. Guan et al., 2009. It's suitable for text classification, especially using the models with their own tokenizers such as BERT, which shows good performance on the plain text.
 
-On the internet, there are [R52 datasets after pre-processing](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, so I built one.
+On the internet, there are [R52 datasets after pre-processing](https://ana.cachopo.org/datasets-for-single-label-text-categorization), provided by Ana Cardoso-Cachopo. However I couldn't find the raw R52 dataset without pre-processing, so I built one.
 
 ## Making a subset
 
-I used dataset provided with NLTK Python library as a base. The only change in the text is that escaped 'less than' sign (\&lt;) is restored to <.
+I used the dataset provided with the NLTK Python library as a base. The only change in the text is that the escaped 'less than' sign (\&lt;) is restored to <.
 
-I tried my best to follow the given directions, but **there are inconsistencies with the R52 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. H. Guan et al. mentioned that their dataset consists of 9,052 documents, which is close to the number of unique documents, 9,053.) So, **please use with caution**.
+I tried my best to follow the given directions, but **there are inconsistencies with the R52 dataset online**. The total number of documents in the other pre-processed R52 dataset is 9,100, whereas mine is 9,130. I'm not sure where this inconsistency comes from. Maybe the NLTK version of Reuters-21578 has some duplicated documents over different categories. (c.f. H. Guan et al. mentioned that their dataset consists of 9,052 documents, which is close to the number of unique documents, 9,053.) So, **please use with caution**.
 
 ## Distribution
 
@@ -79,9 +91,9 @@ There are 4 columns, each are file id, category id, name of the category, and th
 
 Columns are distinguished with tabs (\t).
 
-The category id is given in the alphabetical order.
+The category id is given in alphabetical order.
 
-The raw text (most of them) contains New-line character (\n), so its quoted with quote sign. (")
+The raw text (most of them) contains New-line character (\n), so it's quoted with a quote sign. (")
 
 ## Notes
 
@@ -91,4 +103,4 @@ If you're using Pandas, you can load the file by
 
 ```
 pandas.read_csv('r52-raw.txt', header=None, sep='\t')
-```
+```